Multimorbidity burden and patterns associated with DeepBrainNet‐derived brain–age gap in dementia‐free older adults: A community‐based study

Q: What are the key findings of this study?

Multimorbidity and cardiometabolic multimorbidity were associated with a larger brain-age gap in older adults.

Q: What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Multimorbidity and cardiometabolic multimorbidity in dementia-free older adults (n=1151). Intervention: Multimorbidity burden and patterns (including cardiometabolic multimorbidity) vs. Absence of multimorbidity or cardiometabolic multimorbidity. Primary outcome: Brain-age gap (years) derived from structural brain MRI using the DeepBrainNet model (β 1.57, 95% CI 0.68-2.45, p=<0.05).

Q: What does this research mean for the field?

Multimorbidity and cardiometabolic multimorbidity are significantly associated with accelerated brain aging, manifesting as a larger brain-age gap in dementia-free older adults. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Q: What question did this study set out to answer?

To investigate the associations of multimorbidity burden and specific multimorbidity patterns with the DeepBrainNet-derived brain–age gap among dementia-free older adults.

Xinyu Liu; Ming Mao; Cuicui Liu; Dige Ai; Jiacheng Wang; Tianyu Yu; X Han; Y Q Ren; Xiaolei Han; Y L Dong; Lin Song; Shi Tang; Na Tian; Lin Cong; Ke Xu; Yifeng Du; Chengxuan Qiu; Yongxiang Wang

doi:10.1002/alz.71647

July 1, 2026Alzheimer s & DementiaOpen Access

Multimorbidity burden and patterns associated with DeepBrainNet‐derived brain–age gap in dementia‐free older adults: A community‐based study

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Key result

Multimorbidity linked to a ~1.6-year larger brain-age gap in older adults.

β 1.57
95% CI 0.68-2.45
P<0.05
n=1,151

Figures

Why the study?

Emerging evidence has linked chronic diseases with structural brain measures, but the relationship between multimorbidity patterns and brain-age gap is unclear.

Is the burden and pattern of multimorbidity (including cardiometabolic multimorbidity) associated with an increased brain-age gap in dementia-free older adults?

Key Points

To investigate the associations of multimorbidity burden and specific multimorbidity patterns with the DeepBrainNet-derived brain–age gap among dementia-free older adults.
Analyzed cross-sectional data from 1151 dementia-free older adults (mean age 70.8 years, 58.5% female) in the community-based MIND-China study.
Identified five multimorbidity patterns using hierarchical cluster analysis, defined cardiometabolic multimorbidity as coexistence of two or more cardiometabolic diseases, and estimated predicted brain age using DeepBrainNet.
Assessed associations between chronic disease counts, multimorbidity clusters, and the brain–age gap using multivariable linear regression models.
Total chronic disease count (multivariable-adjusted β = 0.53; 95% CI: 0.34, 0.73), presence of multimorbidity (β = 1.57; 95% CI: 0.68, 2.45), and cardiometabolic multimorbidity (β = 2.47; 95% CI: 1.27, 3.68) were significantly associated with a larger brain–age gap (p < 0.05).
Multimorbidity cluster 1 (cerebrovascular disease, hypertension, dyslipidemia, and diabetes; β = 1.97; 95% CI: 1.17, 2.77) and cluster 4 (biliary tract diseases, dorsopathies, obesity, anemia, and hearing problems; β = 1.25; 95% CI: 0.58, 1.92) were significantly correlated with a larger brain–age gap (p < 0.05).
Individual conditions showing significant associations with advanced brain age included obesity (β = 1.96; 95% CI: 0.78, 3.15), blindness or visual impairment (β = 1.76; 95% CI: 0.56, 2.95), diabetes (β = 1.62; 95% CI: 0.69, 2.55), cerebrovascular disease (β = 1.52; 95% CI: 0.53, 2.51), and hypertension (β = 1.45; 95% CI: 0.74, 2.16).

Study Design

Type

Cross-Sectional (n=1,151)

Multicenter

Yes

Structured PICO

Is the burden and pattern of multimorbidity (including cardiometabolic multimorbidity) associated with an increased brain-age gap in dementia-free older adults?

P

Population

1,151 dementia-free older adults (mean age 70.8 years, 58.5% female) from rural communities in China undergoing brain MRI in a cross-sectional baseline evaluation.

E

Exposure

Multimorbidity (coexistence of ≥2 of 19 chronic health conditions), multimorbidity burden (total number of chronic conditions), cardiometabolic multimorbidity (CMM; coexistence of ≥2 of heart disease, diabetes, and cerebrovascular disease), and 5 data-driven hierarchical multimorbidity clusters.

C

Comparator

Individuals without multimorbidity (0-1 chronic diseases) or without cardiometabolic multimorbidity.

O

Outcome

Brain-age gap (in years), defined as the bias-corrected difference between chronological age and predicted brain age estimated from T1-weighted structural brain MRI using the DeepBrainNet deep learning model.surrogate

Main Result

Mean Difference: 1.57 (95% CI 0.68–2.45)

p-value: p=<0.05

Both overall multimorbidity burden and specific cardiometabolic multimorbidity patterns are associated with an advanced neuroimaging-derived brain-age gap of 1.5 to 2.5 years in dementia-free older adults.

Limitations

Cross-sectional study design precludes causal inference and may be affected by selective survival bias
Participants in the brain MRI sub-study were relatively younger, healthier, and more educated than the total MIND-China sample
Study participants were recruited from a single rural area in western Shandong province, limiting generalizability
Cross-sectional design prevents causal inference and is susceptible to selective survival bias
MRI sub-study participants were relatively younger, healthier, and more educated than the overall MIND-China cohort
Single rural region in western Shandong province limits generalizability to other populations

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

May inform cognitive risk assessment in older adults; leaves open causality and intervention effects on brain aging.

Abstract

INTRODUCTION Emerging evidence has linked chronic diseases with structural brain measures; however, the relationship between multimorbidity patterns and brain–age gap is unclear. METHODS This community‐based study involved 1151 dementia‐free older adults in Multimodal Interventions to Delay Dementia and Disability in Rural China (MIND‐China). Multimorbidity was defined as coexistence of two or more chronic diseases. Hierarchical cluster analysis was used to identify five patterns of multimorbidity. We additionally defined cardiometabolic multimorbidity as coexistence of two or more cardiometabolic diseases. The predicted brain age was estimated using DeepBrainNet. Data were analyzed using linear regression models. RESULTS The number of chronic diseases, multimorbidity, and cardiometabolic multimorbidity were significantly associated with larger brain–age gap ( p < 0.05). The multimorbidity clusters comprising cerebrovascular disease and metabolic disorders or biliary tract diseases, dorsopathies, anemia, and hearing problems were significantly correlated with larger brain–age gap ( p < 0.05). DISCUSSION The overall burden and cardiometabolic pattern of multimorbidity are associated with advanced brain aging in dementia‐free older adults.

Discussion

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Cite This Study

Liu et al. (2026) conducted a cross-sectional in Multimorbidity and cardiometabolic multimorbidity in dementia-free older adults (n=1,151). Multimorbidity burden and patterns (including cardiometabolic multimorbidity) vs. Absence of multimorbidity or cardiometabolic multimorbidity was evaluated on Brain-age gap (years) derived from structural brain MRI using the DeepBrainNet model (β 1.57, 95% CI 0.68-2.45, p=<0.05). Multimorbidity and cardiometabolic multimorbidity were associated with a larger brain-age gap in older adults.

synapsesocial.com/papers/6a4751d25c29257aa2578bd8 https://doi.org/10.1002/alz.71647

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Multimorbidity burden and patterns associated with DeepBrainNet‐derived brain–age gap in dementia‐free older adults: A community‐based study

Figures

Key Points

Study Design

Structured PICO

Main Result

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Multimorbidity burden and patterns associated with DeepBrainNet‐derived brain–age gap in dementia‐free older adults: A community‐based study

Figures

Key Points

Study Design

Structured PICO

Main Result

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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