"Translational Psychiatry": The Yu Jintai team of Huashan Hospital found that the more comorbidities, the higher the risk of dementia!

As the global population ages, dementia has gradually become one of the
major public health problems.
Dementia is a highly heterogeneous disease consisting of multiple subtypes, including Alzheimer's disease (AD) (60-70%) and vascular dementia (VD) (25%), among others [1].

As a major cause of disability, dependency and death, dementia places a heavy burden on patients, families and society [2].

At present, the treatment strategy of dementia is relatively lacking, so early prevention of dementia is particularly important
.

The onset of dementia is influenced by a variety of factors, and long-term conditions (LTCs) such as hypertension, diabetes, atrial fibrillation, and depression have been found to be associated with a higher risk of dementia [3-6].

Several studies have shown that comorbidities (coexistence of multiple LTCs) are associated with accelerated neuronal damage and pathological exacerbation of dementia, and cognitive decline in comorbidities [7-8].

Of note, due to overlapping risk factors or common pathophysiological mechanisms, certain diseases tend to coexist in the same individual, resulting in comorbid patterns characterized by multiple LTCs clustering [9].

However, whether individuals with increased numbers of LTCs experience a higher cumulative risk of dementia has not been fully studied, and there is little evidence on how different comorbid patterns are associated
with the onset of dementia.
In addition, there are few
studies exploring the relationship between comorbidities and dementia subtypes.

Recently, Professor Yu Jintai's team from Huashan Hospital affiliated to Fudan University published important results in Translational Psychiatry, a top journal in the field of psychiatry [10].

The team discovered the effect
of LTS number and specific comorbidity patterns on the risk of all-cause dementia (ACD), AD and VD through large-scale prospective cohort analysis.
This is a stronger evidence of the relationship between comorbidities and dementia, and is conducive to the early prevention
of dementia in specific populations.

Next, let's take a look at how this research
unfolds.

The researchers included 245,483 non-dementia participants (≥ 55 years old) from the UK Biobank and recorded their demographics, lifestyle and LTCs (Table 1).

The participants' disease diagnoses
were determined based on the International Classification of Diseases (ICD)-10 in medical records.
The researchers further recorded that during the follow-up period (median time = 9.
26 years), 5123 (2.
09%) participants developed ACD, 2228 (0.
91%) participants developed AD, and 1234 (0.
50%) participants developed VD.

The researchers excluded LTCs with a prevalence of <1% and included 29 LTCs in the analysis
.
They classified comorbidities into 0 LTC (healthy), 1 LTC, 2 LTCs, 3 LTCs, and ≥4 LTCs
based on the number of LTCs.
Notably, the researchers used the fuzzy C-means clustering algorithm to identify three comorbid patterns: pattern A (n = 46,172), obesity accompanied by other diseases; Mode B (n=24,504), cardiovascular and cerebrovascular diseases, respiratory diseases, metabolic diseases, musculoskeletal diseases, and depression; Mode C (n=67,147), tumors, genitourinary diseases, and digestive disorders (Table 1).

Table 1 Baseline characteristics of the included population

The researchers used the Cox proportional hazards regression model to assess the relationship between the number of LTCs and specific comorbidity patterns and the risk of developing ACD, AD, and VD, and corrected for covariates
such as age, sex, educational level, body mass index (BMI), physical activity, smoking, and APOE4 status.

First, the researchers found that 18, 7, and 24 LTCs were associated with an increased risk of ACD, AD, and VD, respectively (Figure 1).

Fig.
1 Relationship between LTCs and dementia risk in different comorbid modes

Next, they found that the cumulative risk curves of ACD, AD, and VD had a larger gradient in participants with ≥ 2 LTCs compared to healthy controls, and that the risk of developing ACD, AD, and VD was highest in patients with ≥ 4 LTCs (Figure 2).

Importantly, the researchers observed a dose-response relationship
between the number of LTCs and the risk of ACD.

Fig.
2 Association between comorbid status and risk of ACD, AD and VD

The researchers then found that participants with comorbid modes A, B, and C had a higher risk of developing ACD and VD compared to healthy people, and participants with comorbid modes B and C had a higher
risk of developing AD.
Notably, Model B participants had the highest risk of developing ACD, AD, and VD (Figure 2).

Specifically, mode B and mode C had a 46% increased risk of ACD compared with healthy controls (HR = 1.
46, 95% CI: 1.
28-1.
67, P<0.
001) and 11% (HR = 1.
11, 95% CI: 1.
00-1.
24, P=0.
044), respectively.

Participants in mode B had a higher risk of developing AD (HR = 1.
28, 95% CI: 1.
04 to 1.
58, P = 0.
021).

In addition, all three comorbidities showed a significant risk of VD (Model A, HR = 1.
54, 95% CI: 1.
11-2.
14; Mode B, HR=2.
50, 95% CI: 1.
90-3.
27; Mode C, HR=1.
73, 95% CI: 1.
37-2.
18; All P <0.
05) (Figure 2).

Finally, the researchers performed separate subgroup analyses based on sex, age, and APOE4 status, and found that the risk of ACD and AD was more significantly associated with comorbidities in the female population
.
However, age and APOE4 status had no effect on the association of comorbidities with dementia (Figure 3).

Figure 3 Subgroup analysis of the association between comorbid status and ACD by sex, age, and APOE4 status

In this study, the relationship between LTC number and specific comorbidity patterns and the risk of ACD, AD and VD was explored using large sample data with long follow-up time, and the relationship
between comorbidity and dementia was more robustly demonstrated.
In addition, researchers use cluster analysis algorithms to study the overall health status of a specific population, which is conducive to the accurate prevention
of diseases in a specific population.
However, the study failed to explore the interactions of the drugs used by the participants or track changes
in comorbid status during follow-up.

Overall, the comorbidity model proposed in this study contributes to a better understanding of the relationship between
comorbidity and dementia.
Timely identification of comorbidities in specific patterns and prevention of the cumulative occurrence of diseases are of great significance for the primary prevention of dementia, which will help optimize the allocation
of medical resources in the elderly.

References:

[1].
World Health Organization.
Dementia fact Sheet.
2021.
mediacentre/factsheets/fs362/en/.

[2].
Shah H, Albanese E, Duggan C, Rudan I, Langa KM, Carrillo MC, et al.
Research priorities to reduce the global burden of dementia by 2025.
Lancet Neurol.
2016; 15:1285–94.

[3].
Ding M, Fratiglioni L, Johnell K, Santoni G, Fastbom J, Ljungman P, et al.
Atrial fibrillation, antithrombotic treatment, and cognitive aging: a population-based study.
Neurology.
2018; 91:e1732–e1740.

[4].
Ou YN, Tan CC, Shen XN, Xu W, Hou XH, Dong Q, et al.
Blood pressure and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 209 prospective studies.
Hypertension (Dallas, Tex: 1979).
2020; 76:217–25.

[5].
Xue M, Xu W, Ou YN, Cao XP, Tan MS, Tan L, et al.
Diabetes mellitus and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 144 prospective studies.
Ageing Res Rev.
2019; 55:100944.

[6].
Byers AL, Yaffe K.
Depression and risk of developing dementia.
Nat Rev Neurol.
2011; 7:323–31.

[7].
Vassilaki M, Aakre JA, Mielke MM, Geda YE, Kremers WK, Alhurani RE, et al.
Multimorbidity and neuroimaging biomarkers among cognitively normal persons.
Neurology.
2016; 86:2077–84.

[8].
Wei MY, Levine DA, Zahodne LB, Kabeto MU, Langa KM.
Multimorbidity and cognitive decline over 14 years in older Americans.
J Gerontol Ser A Biol Sci Med Sci.
2020; 75:1206–13.

[9].
Marengoni A, Roso-Llorach A, Vetrano DL, Fernández-Bertolín S, Guisado-Clavero M, Violán C, et al.
Patterns of multimorbidity in a population-based cohort of older people: sociodemographic, lifestyle, clinical, and functional differences.
J Gerontol Ser A Biol Sci Med Sci.
2020; 75:798–805.

[10].
Hu HY, Zhang YR, Aerqin Q, et al.
Association between multimorbidity status and incident dementia: a prospective cohort study of 245,483 participants.
Transl Psychiatry.
2022; 12(1):505.
Published 2022 Dec 7.
doi:10.
1038/s41398-022-02268-3