
==== Front
Chin Med J (Engl)
Chin Med J (Engl)
CM9
Chinese Medical Journal
0366-6999
2542-5641
Lippincott Williams & Wilkins Hagerstown, MD

38031345
CMJ-2023-615
10.1097/CM9.0000000000002914
00007
3
Original Article
Development and validation of a nutrition-related genetic–clinical–radiological nomogram associated with behavioral and psychological symptoms in Alzheimer’s disease
Jiang Jiwei 1 2
Liu Yaou 2 3
Wang Anxin 1 2
Zhuo Zhizheng 2 3
Shi Hanping 4 5 6
Zhang Xiaoli 1 2
Li Wenyi 1 2
Sun Mengfan 1 2
Jiang Shirui 1 2
Wang Yanli 1 2
Zou Xinying 1 2
Zhang Yuan 1 2
Jia Ziyan 1 2
Xu Jun 1 2
Gao Ting
Hao Xiuyuan
1 Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
2 National Clinical Research Center for Neurological Diseases, Beijing 100070, China
3 Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
4 Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
5 Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
6 Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing 100081, China
Correspondence to: Jun Xu, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing 100070, ChinaE-Mail: neurojun@126.com
29 11 2023
20 9 2024
137 18 22022212
13 4 2023
Copyright © 2024 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

Abstract

Background:

Few evidence is available in the early prediction models of behavioral and psychological symptoms of dementia (BPSD) in Alzheimer’s disease (AD). This study aimed to develop and validate a novel genetic–clinical–radiological nomogram for evaluating BPSD in patients with AD and explore its underlying nutritional mechanism.

Methods:

This retrospective study included 165 patients with AD from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) cohort between June 1, 2021, and March 31, 2022. Data on demographics, neuropsychological assessments, single-nucleotide polymorphisms of AD risk genes, and regional brain volumes were collected. A multivariate logistic regression model identified BPSD-associated factors, for subsequently constructing a diagnostic nomogram. This nomogram was internally validated through 1000-bootstrap resampling and externally validated using a time-series split based on the CIBL cohort data between June 1, 2022, and February 1, 2023. Area under receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical applicability of the nomogram.

Results:

Factors independently associated with BPSD were: CETP rs1800775 (odds ratio [OR] = 4.137, 95% confidence interval [CI]: 1.276–13.415, P = 0.018), decreased Mini Nutritional Assessment score (OR = 0.187, 95% CI: 0.086–0.405, P <0.001), increased caregiver burden inventory score (OR = 8.993, 95% CI: 3.830–21.119, P <0.001), and decreased brain stem volume (OR = 0.006, 95% CI: 0.001–0.191, P = 0.004). These variables were incorporated into the nomogram. The area under the ROC curve was 0.925 (95% CI: 0.884–0.967, P <0.001) in the internal validation and 0.791 (95% CI: 0.686–0.895, P <0.001) in the external validation. The calibration plots showed favorable consistency between the prediction of nomogram and actual observations, and the DCA showed that the model was clinically useful in both validations.

Conclusion:

A novel nomogram was established and validated based on lipid metabolism-related genes, nutritional status, and brain stem volumes, which may allow patients with AD to benefit from early triage and more intensive monitoring of BPSD.

Registration:

Chictr.org.cn, ChiCTR2100049131.

Keywords:

Alzheimer’s disease
Behavioral and psychological symptoms
Nutrition
Brain stem
Cholesterol ester transfer proteins
Nomogram
OPEN-ACCESSTRUE
SDCT
==== Body
pmcIntroduction

Alzheimer’s disease (AD) is the leading cause of dementia among older adults. Recent epidemiological data have estimated that the number of people with dementia will increase worldwide from 57.4 million cases in 2019 to 152.8 million cases in 2050, highlighting the urgent need for public health planning efforts and policy to address the needs of this group.[1] Behavioral and psychological symptoms of dementia (BPSD) form an important group of disturbances observed in patients with AD that can manifest as agitation, aggression, anxiety, depression, and sleep and appetite difficulties.[2] Approximately 80% of patients with dementia experience one or more of these neuropsychiatric disturbances, which contribute to the overall clinical deterioration of patients and significantly increase of caregiver burden.[3,4] Despite the high prevalence of these symptoms, they are not included in the diagnostic criteria for AD.[5] The clinical response to antidepressants and antipsychotics shows high heterogeneity, with several patients showing a poor response. Moreover, these drug therapies are associated with serious safety concerns.[2,6] Many therapeutic clinical trials for AD have focused on cognitive symptoms and pathological biomarkers, but few have focused on BPSD outcomes.[7,8] Additionally, although systematic reviews of non-pharmacological interventions for BPSD suggest that music therapy and functional analysis-based interventions are effective first-line management for relieving BPSD,[9,10] many of these approaches show gradual effects, and additional high-quality research is required to assess their effectiveness.[11] Therefore, optimal pharmacological and non-pharmacological intervention strategies are yet to be established for patients with BPSD.

The primary reason for the shortcomings of drug treatments for BPSD is the complex and unknown pathogenesis of this aspect of AD. Recent evidence has shown that various biopsychosocial factors, including patient, caregiver, and environmental factors, may interact with and contribute to BPSD.[12] Moreover, the heterogeneity of various BPSD subtypes presents considerable challenges to research into the mechanistic pathways and pharmacological treatment of BPSD. Recent research has demonstrated that numerous factors can serve as predictors or risk factors for specific BPSD subtypes, including age, sex, years of education, marital status, sedative or psychotropic use, and caregiver qualities.[131415] Therefore, management of BPSD in patients with AD can be achieved via two approaches. The first is the exploration of entirely novel disease mechanisms in cohorts of individuals with AD with clinically characterized neuropsychiatric symptoms, to identify better therapeutic options.[16] The second involves the development of reliable diagnostic or predictive models for early detection and intervention of BPSD in patients with AD.

Dietary nutrition is considered to be the most direct and important environmental factor contributing to the development of AD and has been gaining increasing attention as a research topic.[17,18] The Nutrition for Dementia Prevention Working Group recently summarized that dietary nutrition could directly or indirectly affect the brain of patients with dementia through complex interactions of various biological pathways, including behavioral, genetic, systemic, and brain factors, consistent with the multifaceted nature of BPSD.[19] An observational study including patients with mild cognitive impairment (MCI) and early AD demonstrated that nutritional status was significantly associated with the incidence of specific BPSD subtypes, such as “verbal aggressiveness/emotional disinhibition” and “apathy”.[20] Furthermore, a longitudinal study with a 2.5-year follow-up period revealed that malnutrition or the risk of malnutrition could increase BPSD incidence in older women with MCI and early stage AD.[21]

Despite accumulating evidence of its importance, little is known regarding the nutritional status of patients with BPSD. A multicenter, randomized, double-blind, placebo-controlled phase II clinical study in patients with mild to moderate AD suggested that daily supplementation with the nutraceutical significantly improved apathy and ameliorated neuropsychiatric distress.[22] However, a meta-analysis demonstrated no significant impact of nutritional supplementation on BPSD.[23] A recent multisite, randomized, double-blind, placebo-controlled trial revealed that omega-3 unsaturated fatty acid supplements did not ameliorate cognitive, functional, or depressive symptom outcomes in patients with MCI or AD.[24] A lack of information regarding BPSD in patients with AD limits the conclusions that can be drawn on relevant aspects. The multi-etiological nature and clinical heterogeneity of BPSD are crucial factors hampering the investigation of the role of nutrition in BPSD progression. The aim was to develop and validate a nutrition-related genetic–clinical–radiological nomogram for BPSD in patients with AD to better understand the relationship between BPSD and nutrition.

Methods

Study participants

The data used in this study were acquired from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) cohort, which is an ongoing, large-scale, prospective cohort study evaluating the risk factors, biomarkers, and neuroimaging of individuals with AD in the Chinese population. The CIBL study was registered at chictr.org.cn, ChiCTR2100049131, https://www.chictr.org.cn/index.html. The Institutional Review Board of Capital Medical University, Beijing Tiantan Hospital, approved the CIBL study (No. KY-2021-028-01). All participants and their caregivers provided written informed consent to participate in the study.

The inclusion criteria of this study were: (1) age between 55 years and 85 years; (2) meeting the 2011 or 2018 National Institute on Aging Alzheimer’s Association workgroup diagnostic criteria for probable AD;[25,26] (3) objective cognitive impairment according to the Chinese version of the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) with the diagnostic threshold adjusted for years of education; and (4) demonstrating a significant decline in daily living abilities based on the activities of daily living (ADL) scale. The following exclusion criteria were applied: (1) core features of other central neurodegenerative diseases, including dementia with Lewy bodies, frontotemporal dementia, or Parkinson’s disease; (2) concomitant central nervous system diseases potentially causing cognitive impairment, such as cerebrovascular disease, tumor, encephalitis, or epilepsy; (3) history of a mental disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5;[27] (4) cognitive impairment due to traumatic brain injury; (5) history of drug abuse or exposure to toxic environments; (6) systemic diseases, such as syphilis or human immunodeficiency virus; and (7) lack of clinical, genetic, or imaging data.

Nested case-control study

Patients with AD from the CIBL cohort were enrolled in the training cohort between June 1, 2021, and March 31, 2022. In the training dataset, we established a nested case-control study of 110 patients with AD with BPSD. Controls were randomly selected from the participants with AD but without BPSD at baseline and were matched with cases by age (±1 year) and sex using a 1:2 ratio. Overall, 110 incident cases and 55 matched controls were included in this analysis to develop the model, after excluding patients with missing data. Patients with AD from the CIBL cohort between June 1, 2022, and February 1, 2023, were included for the external validation of the model. Figure 1 shows the selection criteria of the enrolled participants in the training and validation cohorts.

Figure 1 Flow diagram showing inclusion and exclusion criteria of the development and validation of the nomogram study. AD: Alzheimer’s disease; ADL: Activities of Daily Living; BPSD: Behavioral and psychological symptoms of dementia; CIBL: Chinese Imaging, Biomarkers, and Lifestyle study; DLB: Dementia with Lewy bodies; FTD: Frontotemporal dementia; HIV: Human immunodeficiency virus; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; NIA-AA: National Institute on Aging Alzheimer’s Association; PDD: Parkinson’s disease dementia.

Data collection

Clinical data

This study collected demographic data, including participant age at initial enrollment, sex, marital status, years of education, body mass index (BMI), and waist-to-hip ratio (WHR). Medical history was also collected, including data on hypertension, diabetes mellitus, dyslipidemia, and cerebrovascular disease.

Marital status was defined as being married, and other types of civil status included widowhood, separation, divorce, and never married. BMI, commonly used as a predictor of nutritional status and overall adiposity,[28] was calculated by dividing the weight of each participant in kilograms by height in meters squared. WHR was calculated as waist circumference divided by hip circumference and was included because central obesity is associated with metabolic dysfunction and structural abnormalities in the brain.[29] Hypertension was defined as a systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or the use of antihypertensive medication within the previous 2 weeks.[30] Diabetes mellitus was diagnosed based on the presence of any two of the following abnormal screening test results: fasting plasma glucose ≥7.0 mmol/L, 2 h plasma glucose ≥11.1 mmol/L during a 75 g oral glucose tolerance test, or glycosylated hemoglobin A1c (HbA1c) ≥6.5%.[31] Dyslipidemia was defined as taking lipid-lowering medications or the presence of lipid abnormalities, including increased total cholesterol (≥6.20 mmol/L), increased low-density lipoprotein cholesterol (LDL-C; >4.13 mmol/L), increased triglyceride (TG; >2.25 mmol/L), or decreased high-density lipoprotein cholesterol (HDL-C; <1.03 mmol/L) levels.[32] Cerebrovascular disease was defined as a medical history of stroke or transient ischemic attack.[33]

Comprehensive neuropsychological and nutritional assessments

All participants underwent comprehensive neuropsychological and nutritional assessments, including the MMSE, MoCA, neuropsychiatric inventory (NPI), ADL scale, Mini-Nutritional Assessment (MNA), and caregiver burden inventory (CBI). A trained neurologist or neuropsychologist administered the neuropsychological tests.

Over the month before the study, BPSD was assessed using the NPI scale, which is a fully structured interview investigating 12 behavioral and neuropsychiatric domains, including delusions, hallucinations, agitation, depression, anxiety, euphoria, apathy, disinhibition, irritability, aberrant motor activity, sleep disturbances, and appetite disturbances.[34] Global objective cognition was assessed using the Chinese version of the MMSE[35] and the Beijing version of the MoCA.[36] Participants with a total MMSE score of ≤24, ≤20, and ≤17 with >6 years, 1–6 years, and 0 years of education, respectively, or a total MoCA score of ≤24, ≤20, and ≤14 with >6 years, 1–6 years, and 0 years of education, respectively, were considered to have cognitive impairment. The ADL questionnaire includes 10 items that assess basic ADL tasks and 10 items that examine instrumental ADL tasks. The caregiver completed the ADL questionnaire for the evaluation of the patient’s abilities in their basic ADL. Each item is rated 1–4, and a higher score (for items and total [ranging from 20 to 80]) implies a greater impairment of ADL functions. The MNA scale is a single, rapid assessment of the nutritional status of older patients in outpatient clinics, hospitals, and nursing homes. It comprises simple measurements and brief questions, including anthropometric measurements, a global assessment, a dietary questionnaire, and a subjective assessment.[37] The higher the MNA total score, the better the nutritional status. The CBI is a 24-item multidimensional questionnaire that quantifies the caregiver burden in the following five domains: time-dependent, developmental, physical, social, and emotional.[38] Each question is rated using a 5-point Likert scale ranging from 0 (not at all descriptive) to 4 (very descriptive). The total CBI score ranges from 0 to 96, with higher scores indicating greater levels of perceived burden.

Genetic data

All genetic data were assessed at the WeGene Lab (Shenzhen, China) using a customized Illumina WeGene V3 Array that contains approximately 700,000 markers (Illumina iScan System; Illumina, Inc., San Diego, CA, USA). We collected the following data: single-nucleotide polymorphism (SNP) sites of AD risk genes affecting lipoprotein metabolism, including APOE, CLU, SORL1, ABCA7, MTHFR, and CETP; and COMT which is involved in dopamine metabolism and neurotransmission. The APOE genotype was determined as previously described by Forlenza et al[39] using two SNPs (rs7412 and rs429358).

Structural magnetic resonance imaging (MRI)

Imaging acquisition was performed using a 3.0-T magnetic resonance scanner (SIGNA Premier; GE Healthcare, Milwaukee, WI, USA) with a 48-channel head coil. High-resolution three-dimensional (3D) T1 scans were acquired using the inversion recovery gradient recalled echo sequence with the following parameters: repetition time = 7.3 ms, echo time = 3.0 ms, inversion time = 450 ms, flip angle = 12°, field of view = 256 mm × 256 mm, acquisition matrix = 256 × 256, slice thickness = 1.0 mm, slice number = 176, and scan time = 4 min 56 s.

Brain structures in the limbic system were segmented and measured from the 3D T1 MRI and processed using Freesurfer software (version 7.3; http://surfer.nmr.mgh.harvard.edu/) by a junior radiologist with 5 years of experience in neuroradiology, then assessed by a senior radiologist with 20 years of experience in neuroradiology. The “recon-all” pipeline was used for preprocessing, which included (1) Talairach transformation; (2) intensity normalization; (3) skull stripping; (4) gray matter, white matter, and cerebrospinal fluid segmentation; and (5) extraction of limbic system volume. Additionally, visual assessments of the Talairach transformation and manual correction were conducted to ensure segmentation accuracy.

The brain regions of interest (ROIs) in this study were the regions involved in both the homeostatic regulation of appetite and the cognitive control of eating, consistent with previous reports.[404142] Therefore, the volumes of 24 dietary nutrition-related ROIs were collected and analyzed in this study, including the bilateral hypothalamus and its subregions (bilateral anterior inferior hypothalamus, anterior superior hypothalamus, posterior hypothalamus, tubular inferior hypothalamus, and tubular superior hypothalamus), amygdala, insula, caudate nucleus, putamen, nucleus accumbens (NAc), and brain stem, as well as the estimated total intracranial volume.

Statistical analyses

All statistical analyses were performed using SAS Version 9.4 software (SAS Institute, Inc., Cary, NC, USA) and R (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria). The development and validation of the nomogram included in three steps. First, the clinical, genetic, and radiological factors were selected using univariate and multivariate analyses. In the univariate analysis, chi-squared or Fisher’s exact test was used to assess statistically significant differences in categorical variables, while continuous variables were compared using independent t-tests for data following a normal distribution or the Mann–Whitney U test for skewed data. The radiological features with a P value <0.01 in the univariate analysis were selected for the least absolute shrinkage and selection operator (LASSO) regression analysis, which avoids the problems of multicollinearity and overfitting that arise when too many factors are inserted in the model. We used threefold cross-validation, which can centralize and normalize the included variables, to select the best lambda value (0.046). A model with good performance and minimal independent variables was constructed using “lambda + 1 se” (0.155). The clinical and genetic features with a P value <0.05 in the univariate analysis and the radiological features with a P value <0.05 in the LASSO regression were selected for the subsequent multivariate conditional logistic stepwise regression (αin = 0.10, αout = 0.05). Data were subjected to min–max normalization to minimize the non-biological variations in different dimensional samples in the multivariate models.

Second, we developed a nomogram for BPSD in patients with AD based on the findings of the multivariate stepwise logistic regression model. The final multivariable model for diagnosing the probability of BPSD in patients with AD was derived using the formula:Logit(P) = β1X1 + β2X2… + βjXj, where β1–βj are the regression coefficients, X1–Xj are the reported values of the covariates showing association in multivariable logistic regression, and P represents the probability of BPSD.[43] Regression coefficients were used to construct the variable axes in the nomogram.

Third, 1000 bootstrap resamples were set to internally validate the stability of the model by randomly splitting the patients in the training cohort into ten equal samples. Eight of these samples were used to construct logistic regression models, and the model coefficients were applied to the remaining samples. This process was repeated 100 times. A time-series split was applied to externally validate the model’s generalizability by randomly splitting the patients from the CIBL cohort between June 1, 2022, and February 1, 2023. The discriminatory power of the model was assessed by calculating the area under the receiver operating characteristic (ROC) curve and its 95% confidence intervals (CIs). The calibration curve was plotted to evaluate the agreement between nomogram-derived probability and actual observations of the model. Decision curve analysis (DCA) was used to analyze the clinical usefulness of the model. A two-tailed P <0.05 was considered statistically significant in all analyses.

Results

Baseline clinical characteristics

Table 1 shows the clinical characteristics of the 110 patients with AD with BPSD and 55 patients with AD without BPSD in the training cohort.

Table 1 Clinical characteristics of the patients with and without BPSD in the training cohort.

Items	BPSD (n = 110)	Control (n = 55)	t/χ2/Z	P values	
Demographics					
Age (years)	69.5 ± 8.2	69.3 ± 8.1	–0.01*	0.995	
Sex (male)	48 (43.6)	24 (43.6)	0.00†	1.000	
Marital status (married)	91 (82.7)	45 (81.8)	0.02†	0.855	
Education (years)	10.0 (8.0, 12.0)	10.0 (9.0, 12.0)	–0.11‡	0.912	
BMI (kg/m2)	23.2 ± 3.5	24.0 ± 3.2	–1.51*	0.133	
WHR	0.9 (0.8, 0.9)	0.9 (0.8, 0.9)	–0.52‡	0.605	
Medical history					
Hypertension (yes)	53.0 (48.2)	22.0 (40.0)	0.99†	0.320	
Diabetes mellitus (yes)	19.0 (17.3)	10.0 (18.2)	0.02†	0.885	
Dyslipidemia (yes)	55.0 (50.0)	27.0 (49.1)	0.01†	0.912	
Cerebrovascular disease (yes)	23.0 (20.9)	13.0 (23.6)	0.16†	0.689	
Neuropsychological battery					
MMSE (score)	17.0 (9.8, 22.0)	23.0 (19.0, 25.0)	–4.70‡	<0.001	
MoCA (score)	11.5 (5.8, 17.0)	17.0 (13.0, 20.0)	–4.67‡	<0.001	
ADL (score)	29.0 (22.0, 40.3)	22.0 (20.0, 25.0)	–5.16‡	<0.001	
MNA (score)	21.5 (19.0, 24.0)	24.5 (23.0, 27.0)	–5.90‡	<0.001	
CBI (score)	25.5 (13.8, 41.3)	0 (0, 10.0)	–7.59‡	<0.001	
*Data are presented as the mean ± standard deviation, and compared using independent t-tests; † data are presented as n (%) and compared using chi-squared test; ‡data are presented as the median (Q1, Q3) and compared using the Mann–Whitney U test. ADL: Activities of daily living; BMI: Body mass index; BPSD: Behavioral and psychological symptoms of dementia; CBI: Caregiver burden inventory; MMSE: Mini-Mental State Examination; MNA: Mini-Nutritional Assessment; MoCA: Montreal Cognitive Assessment; WHR: Waist-to-hip ratio.

The MMSE scores were lower in the BPSD group than in the controls (median [Q1, Q3]: 17.0 [9.8, 22.0] vs. 23.0 [19.0, 25.0], Z = –4.70, P <0.001); however, ADL and CBI scores were higher in the BPSD group than in the controls (median [Q1, Q3]: 29.0 [22.0, 40.3] vs. 22.0 [20.0, 25.0], Z = –5.16, P <0.001; 21.50 [13.8, 41.3] vs. 0 [0, 10.0], Z = –7.59, P <0.001). The MNA scores were positively correlated with nutritional status and were lower in patients with BPSD (median [Q1, Q3]: 21.5 [19.0, 24.0] vs. 24.5 [23.0, 27.0], Z = –5.90, P <0.001). However, no significant intergroup differences were observed in age, sex, marital status, education, BMI, WHR, or the prevalence of classical vascular/dementia risk factors (hypertension, diabetes mellitus, dyslipidemia, and cerebrovascular disease).

Analyzed SNPs and BPSD risk

Supplementary Table 1, http://links.lww.com/CM9/B843 summarizes the results of the SNP analysis for the two groups in the training cohort. The AD risk genes affecting lipoprotein metabolism demonstrated no intergroup differences in allelic or genotypic distribution. A significantly higher proportion of patients carried the rs4680 variants polymorphism in COMT in the BPSD group than in the control group (50.9% [56/110] vs. 32.7% [18/55], respectively; χ2 = 4.90, P = 0.027). Significant increases were found in the distribution of genotypic frequency (G/G vs. A/A + A/G) in the rs708272 polymorphism (70.0% [77/110] vs. 47.3% [36/55], respectively; χ2 = 8.07, P = 0.005) and (C/C vs. C/A + AA) in the rs1800775 polymorphism (80.9% [89/110] vs. 65.5% [36/55], respectively; χ2 = 4.77, P = 0.029) in the BPSD group.

Brain volumes in ROIs

Table 2 sets out the differences in the 24 brain volumes of ROIs between patients with BPSD and those without. Thirteen ROIs with significant intergroup differences in the univariate analysis were then selected for the subsequent LASSO regression analysis, including the bilateral insula, brain stem, bilateral putamen, bilateral amygdala, bilateral NAc, bilateral anterior inferior hypothalamus, right anterior inferior hypothalamus, and right anterior superior hypothalamus (P <0.010). Among the 13 relevant radiological variables, eight potential factors were identified by the LASSO regression model [Supplementary Figure 1, http://links.lww.com/CM9/B843], including the volumes of the right insula, right putamen, brain stem, bilateral NAc, left amygdala, left anterior inferior hypothalamus, and right anterior inferior hypothalamus.

Table 2 Volumes of 24 brain ROIs: comparison between patients with and without BPSD.

Volume of ROIs (mm3)	Control group (n = 55)	BPSD group (n = 110)	t/Z	P value	
Left insula	5344.2 ± 757.2	4973.2 ± 760.3	2.96*	0.004	
Right insula	5622.2 ± 773.9	5200.6 ± 847.5	3.10*	0.002	
Left caudate nucleus	3109.5 (2870.8, 3380.8)	3100.0 (2684.8, 3330.2)	1.58†	0.115	
Brain stem	19673.1 ± 2339.5	18494.8 ± 2144.6	3.23*	0.002	
Left putamen	4330.2 ± 615.7	3830.8 ± 769.7	4.19*	<0.001	
Right putamen	4479.2 ± 668.0	3902.0 ± 839.6	4.44*	<0.001	
Left amygdala	1266.7 ± 333.4	1120.3 ± 263.8	3.07*	0.003	
Left NAc	373.8 ± 123.9	300.6 ± 113.5	3.79*	<0.001	
Right caudate nucleus	3270.5 (2988.3, 3556.9)	3199.9 (2812.5, 3628.5)	1.52†	0.128	
Right amygdala	1490.8 ± 354.2	1339.5 ± 2734.0	3.03*	0.003	
Right NAc	434.4 (360.8, 470.2)	362.3 (305.0, 432.8)	3.59†	<0.001	
Left anterior inferior hypothalamus	17.5 (15.3, 22.3)	15.9 (13.2, 18.6)	3.35†	0.001	
Left anterior superior hypothalamus	22.0 ± 3.9	19.2 ± 5.5	3.39*	0.001	
Left posterior hypothalamus	111.3 (103.6, 125.2)	107.6 (95.9, 116.9)	2.44†	0.015	
Left tubular inferior hypothalamus	145.4 (127.8, 157.1)	144.3 (134.9, 157.6)	0.03†	0.972	
Left tubular superior hypothalamus	99.3 (92.3, 110.6)	94.5 (88.1, 105.2)	2.07†	0.038	
Right anterior inferior hypothalamus	16.5 ± 4.8	14.2 ± 4.1	3.25*	0.001	
Right anterior superior hypothalamus	19.7 ± 3.7	17.8 ± 3.9	2.93*	0.004	
Right posterior hypothalamus	102.8 (94.3, 116.4)	101.1 (87.7, 108.9)	1.80†	0.072	
Right tubular inferior hypothalamus	129.7 (116.1, 146.4)	132.6 (121.6, 143.0)	–0.64†	0.518	
Right tubular superior hypothalamus	99.9 ± 14.3	94.5 ± 15.5	2.15*	0.033	
Left hypothalamus	397.9 (362.8, 427.2)	385.3 (354.0, 410.5)	2.09†	0.037	
Right hypothalamus	372.3 (342.2, 394.1)	361.0 (335.2, 384.4)	1.48†	0.137	
Estimated TIV (×103)	1439.4 (1331.5, 1576.1)	1410.4 (1337.8, 1511.9)	0.77†	0.443	
*Data are presented as mean ± standard deviation, and compared using independent t-tests; †data are presented as median (Q1, Q3) and compared using the Mann–Whitney U test. BPSD: Behavioral and psychological symptoms of dementia; NAc: Nucleus accumbens; ROIs: Regions of interest; TIV: Total intracranial volume.

Multivariate conditional logistic regression analysis and nomogram development

Finally, 15 factors associated with BPSD in patients with AD in the univariate analysis were included in the multivariate logistic regression analysis, including five clinical factors (MMSE, MoCA, ADL, MNA, and CBI scores), three genetic factors (COMT rs4680 variants, CETP rs1800775 variants, and CETP rs708272 variants), and eight radiological factors (the volumes of the right insula, right putamen, brain stem, bilateral NAc, left amygdala, left anterior inferior hypothalamus, and right anterior inferior hypothalamus). The following factors were found to be independently associated with BPSD in patients with AD: carrier status for the CETP rs1800775 variants (odds ratio [OR] = 4.137, 95% CI: 1.276–13.415, P = 0.018), lower MNA score (OR = 0.187, 95% CI: 0.986–0.405, P <0.001), higher CBI score (OR = 8.993, 95% CI: 3.830–21.119, P <0.001), and decreased brain stem volume (OR = 0.006, 95% CI: 0.001–0.191, P = 0.004).

Based on the final multivariable model, we developed a nutrition-related genetic–clinical–radiological nomogram to evaluate the probability of BPSD in patients with AD that included the CETP rs1800775 variants, MNA and CBI scores, and brain stem volume. The total score was calculated as follows: ([–4.342 × MNA score] + 130.268) + 14.580 ×(if CETP rs1800775 variants) + (109.379 – [0.004 × brain stem volume]) + 1.111 × CBI score. Figure 2 demonstrates the nomogram and describes its use to determine the total scores and probability of BPSD in patients with AD from the training cohort. The optimal cut-off value of the nomogram calculated using the Youden index was 0.623. The optimal cut-off points for the MNA score, CBI score, and brain stem volume were 22.5, 11.0, and 18771.6 mL3, respectively.

Figure 2 Nomogram for evaluating the presence of BPSD in patients with AD. Patient evaluable points are located on the axis of each variable, and a line is then drawn upwards at 90° to determine the number of points for that particular variable. The sum of these numbers is located on the total points axis, and a line is drawn at 90° downward to the risk-of-BPSD axis to determine the likelihood of BPSD in patients with AD. AD: Alzheimer’s disease; BPSD: Behavioral and psychological symptoms of dementia; CBI: Caregiver burden inventory; MNA: Mini-Nutritional Assessment.

Internal and external validation of the nomogram

By internal validation using 1000-bootstrap resampling, the mean AUC of the nomogram was 0.925 (95% CI: 0.884–0.967) with high sensitivity and specificity of 89.1% and 83.6%, respectively, which indicated good discrimination [Figure 3A]. The calibration curve (the mean absolute error = 0.025) suggested that the nomogram had good calibration and fit in the internal validation set [Figure 3B]. In the DCA, the threshold probabilities for the standardized net benefit associated with the application of the nomogram to detecting BPSD ranged from 0.03 to 0.97 [Figure 3C].

Figure 3 Performance of the nomogram in internal and external validation. (A) The area under the ROC curve of internal validation of the nomogram was 0.925 (95% CI: 0.884–0.967). (B) The calibration plots after bootstrapping (1000 samples) show the apparent (actual), bias-corrected (adjusted), and ideal (100% agreement) curves of the nomogram. (C) In the DCA, the y-axis measures the net benefit in the internal validation cohort, the black line represents the assumption that no patients have BPSD, the gray curved line represents the assumption that all patients have BPSD, and the red dotted line represents the risk prediction by the nomogram. (D) The external validation of the nomogram by ROC curve was 0.791 (95% CI: 0.686–0.895). (E) The calibration plots also suggest good agreement. (F) DCA of the nomogram in the external validation also demonstrates a wide threshold probability of BPSD in patients with AD. AD: Alzheimer’s disease; BPSD: Behavioral and psychological symptoms of dementia; ROC: Receiver operating characteristic; CI: Confidence interval; DCA: Decision curve analysis.

Overall, 103 patients with AD were finally included for external validation of the nomogram, including 79 and 24 with and without BPSD, respectively. The clinical characteristics and differences between the training and validation cohorts are presented in Supplementary Table 2, http://links.lww.com/CM9/B843. No significant difference was observed in the clinical characteristics, brain stem volumes, or CETP rs1800775 variants between the training and validation cohorts. The area under the ROC curve remained stable in the external validation (0.791, 95% CI: 0.686–0.895; Figure 3D) with sensitivity and specificity of 60.6% and 92.9%, respectively. The calibration plots of the nomogram showed high consistency between the ideal line and the observed survival probability in the external validation cohorts [Figure 3E]. In the external validation, the DCA revealed that the nomogram had an overall net benefit within a wide threshold probability (0.28–0.90) [Figure 3F].

Discussion

In this study, we developed and validated a reliable and simple-to-use nomogram, encompassing a set of nutrition-related genetic–clinical–radiological risk factors associated with BPSD in patients with AD. Our findings provide a model to help clinicians evaluate the presence of BPSD in patients with AD for early intervention in clinical practice. These findings also preliminarily revealed the potential multidimensional role of nutrition in BPSD and provided novel insights into possible strategies for early detection and nutritional intervention in patients with AD and BPSD.

Recently, nomograms have been used as a predictive method for patients with cancer and stroke due to their ability to reduce statistical predictive models into a single numerical estimate of the likelihood of an event tailored to an individual patient.[44,45] Current evidence for using nomograms as a predictive model in dementia is limited, particularly in patients with BPSD.[46] Considering the limitations of pharmacological and non-pharmacological interventions for BPSD, developing a reliable predictive model for early detection and personalized management of BPSD is critical. The nomogram described herein provides better discriminative and calibration capabilities and may be more clinically applicable than those described in previous studies.[47]

Recent evidence suggests that nutrition and diet are important modifiable environmental factors in the onset and development of AD.[48] However, few studies have explored the association between nutrition and BPSD, and current research tends to focus on sub-symptoms of BPSD. An observational study including participants with MCI and early AD showed that nutritional status was significantly associated with specific BPSD sub-symptoms, including verbal aggressiveness, emotional disinhibition, and apathy.[20] A longitudinal, observational cohort study demonstrated that patients at risk of malnourishment and malnourished patients had higher total NPI scores than did well-nourished patients.[49] This finding indicated that lower MNA scores were independently associated with BPSD in patients with AD, which was consistent with the findings reported by Kishino et al[21].

The complex etiology and underlying mechanism of the relationship between malnutrition and BPSD remain unclear. Considering the multifactor and multiple pathways of BPSD, we evaluated the SNPs of nutrition- and metabolism-related risk genes and structural changes in feeding- and psycho-behavior-related brain regions. Notably, the CETP rs1800775 variants and brain stem atrophy were independent predictors of BPSD in patients with AD. The CETP gene is a well-known key determinant in lipid metabolism, primarily for HDL-C and TG. An abnormal lipid profile is an important risk factor for AD, and evidence suggests that SNPs in CETP contribute to variations in lipid levels in response to dietary intake.[50] Previous studies found specific polymorphisms in CETP that were not only risk and predictive factors of AD development but also protective variations, demonstrating the different roles of CETP polymorphisms in AD.[515253]

In this study, we found that the common CETP polymorphism CETP rs1800775 variants increased the risk of BPSD in patients with AD. Although no mediating effect of malnutrition was found between the CETP variant and BPSD, several studies have demonstrated the regulation and influence of CETP on metabolism and nutrition. For example, a previous study detected elevated adipose lipolysis rates and whole-body energy expenditure in CETP mutant mice, suggesting a novel anti-adipogenic role for CETP.[54] The CORonary Diet Intervention with Olive Oil and Cardiovascular PREVention (CORDIOPREV) clinical trial demonstrated the potential contributing role of CETP rs3764261 in triggering lipid metabolism when combined with a Mediterranean diet.[55] Moreover, the CETP rs1800775 variant is associated with metabolic syndrome at a genome-wide significance level.[56] These findings suggest that nutrigenomic and nutrigenetic regulation participate in the onset and progression of BPSD. Therefore, considering the complex nature of gene–environment interactions, further research is needed to assess the interactions between CETP SNPs and nutritional factors in BPSD pathogenesis.

This study also found that brain stem atrophy was a significant risk factor associated with BPSD in patients with AD. The brain stem is an important brain region with various feeding- and psycho-behavior-related functions. Nutritional balance and energy homeostasis are fundamental determinants of survival and brain health; therefore, individuals need a broad diversity and distribution of neuronal networks to regulate nutritional energy intake and expenditure.[57] Although the hypothalamus has been widely studied for its ability to influence feeding behavior, emerging data suggest that several extrahypothalamic regions can also connect circuits controlling feeding and energy balance with higher brain functions and degenerative processes.[58] Several important subregions within the brain stem can participate in nutrition intake, energy utilization, and behavioral and psychological symptoms, including the nucleus of the tractus solitarius (NTS), the raphe nuclei, the locus coeruleus (LC), and the ventral tegmental area (VTA) of the midbrain.

The NTS is the central target of vagal afferents and numerous distinct neuronal populations and has been implicated in food intake regulation.[59] The vagus nerve relays information on the quantity and quality of nutrients in the gastrointestinal tract to the NTS.[60] A rat model study found that oxytocin administration into the hindbrain effectively reduced food intake, weight gain, and adiposity, with downstream targets being catecholamine non-catecholaminergic NTS neurons.[61] Accordingly, the caudal nucleus of the solitary tract in the brain stem, an important relay station, has been found to participate in the development of fat and sugar preferences, a subtype of BPSD.[62] Recent evidence has shown that specific neurons within the cNTS have crucial roles in organizing neural responses to various behavioral and physiological stress responses.[63] Moreover, research in a murine model has shown that glucose administration suppressed intra-VTA ghrelin-induced feeding, while leptin (a longer-term signal of positive energy balance) did not affect such feeding, which suggested that VTA circuits were sensitive to the integration of signals reflecting the internal homeostatic state and influencing food intake.[64]

Another animal study revealed that photoactivation of gamma-aminobutyric acid-expressing (GABAergic) neurons in the VTA drove a series of anxiety-like behaviors; VTA GABAergic neurons sending abundant projections to the lateral hypothalamus induced overconsumption of palatable food.[65] An observational study that recruited 169 patients with either AD or amnestic MCI due to AD found that BPSD such as agitation, irritability, and disinhibition were associated with VTA connectivity with the parahippocampal gyrus and cerebellar vermis, while sleep and eating disorders were associated with VTA connectivity with the striatum and the insular cortex.[66] These findings suggest a potential role of the VTA linking BPSD and dietary nutrition. In addition, noradrenergic projections to the forebrain originating from the LC regulate many aspects of cognition and behavior. Studies have demonstrated the role of the LC in aging, specifically a relationship between reduced LC structural integrity and impaired cognitive and behavioral function, and have suggested that this nucleus is the initial site of Tau aggregation.[67,68] Another recent study found that LC activation to the lateral hypothalamus suppressed feeding and enhanced avoidance and anxiety-like responses, suggesting an association between emotional responses and feeding via modulation by noradrenergic neurons of the LC.[69] Therefore, we suggest that the nutritional–psychobehavioral circuitry of the central nervous system may be involved in BPSD regulation through its effects on nutritional status and energy balance.

Although the nomogram performed well, this study has some limitations. First, we used a nested, case–control study design based on the CIBL cohort; thus, the nomogram had limited predictive value. Nonetheless, the nomogram can evaluate the associations and the probability of BPSD in patients with AD. Second, this study did not use genome-wide association studies to screen for whole nutrition-related risk genes and did not use serum lipid metabolism-related biomarkers to confirm our hypothesis about the regulatory mechanism of BPSD. Third, because of the limitations of the current brain atlas, we analyzed only the volume changes of the entire brain stem. Therefore, changes in structures and functions of important subregions in the brain stem, including the NTS, VTA, and LC, require further analysis.

In conclusion, we developed and validated a novel and reliable nomogram comprising CETP rs1800775variants carrier status, MNA score, CBI score, and brain stem volume to evaluate the probability of BPSD in patients with AD, thereby providing a critical time window for early detection and intervention. Moreover, this genetic–clinical–radiological model underscored the potential multidimensional impact of nutrition on BPSD, as nutrition- and metabolism-related genes and brain regions may participate in regulating the nutritional status, thereby affecting BPSD; this is a new insight into the nutritional management of BPSD. A multicenter, independent clinical validation study to evaluate the external utility of our nomogram is warranted. Further analyses of the functional characteristics of various nutrition and metabolism-related brain regions are needed to understand the mechanisms underlying nutrition in patients with AD and BPSD.

Acknowledgements

The authors thank all participants of the present study, as well as all members of staff of the CIBL study for their role in data collection and analysis.

Funding

This work was supported by grants from the National Key Research and Development Program of China (Nos. 2021YFC2500100 and 2021YFC2500103) and the National Natural Science Foundation of China (Nos. 82071187 and 81870821).

Conflicts of interest

None.

Supplementary Material

How to cite this article: Jiang JW, Liu YO, Wang AX, Zhuo ZZ, Shi HP, Zhang XL, Li WY, Sun MF, Jiang SR, Wang YL, Zou XY, Zhang Y, Jia ZY, Xu J. Development and validation of a nutrition-related genetic–clinical–radiological nomogram associated with behavioral and psychological symptoms in Alzheimer’s disease. Chin Med J 2024;137:2202–2212. doi: 10.1097/CM9.0000000000002914
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