Metabolic Syndrome, Executive Dysfunction, and Late‐Onset Depression: Just a Matter of White Matter?
Bibliographic record
Abstract
To the Editor: Depression and cognitive disorders often overlap in older individuals, bringing with them a heavy burden of disability. The causal direction of such association has been widely questioned.1-3 Both conditions appear to be tightly associated with cardiovascular and metabolic disorders.1, 2, 4-8 Metabolic syndrome (MetS), a cluster of cardio-metabolic risk factors comprising peripheral inflammation and insulin resistance, is risky for the aging brain4-8 and has been associated with white matter damage. MetS may promote cognitive and depressive disorders independently of the extent of brain vascular damage.4 There is a lack of consensus as to whether white matter hyperintensities (WMHs) may modulate the association between cognitive decline and depression. Depression is often the earliest sign of an approaching cognitive disorder, and symptoms of cognitive loss may characterize the course of mood disorders.1-3 The goal of the current study was to explore whether subjects with late-onset depression had poorer global and domain-specific cognitive functioning than controls, whether MetS was positively associated with more depressive symptoms, and whether such an association was independent of WMH severity. Thirty subjects with current major depression and 15 age- and sex-matched controls (age 78.1 ± 9.2, 29 women) were selected from among community-dwelling individuals referred for evaluation of cardiovascular risk factors. Major depression was diagnosed according to the Structured Diagnostic Interview of the Diagnostic and Statistic Manual of Mental Disorders, Fourth Edition, for depression. Subjects in both groups were consecutively enrolled if they did not have any of the following conditions: dementia; mood disorder with onset before aged 60; cerebrovascular or coronary artery disease, diabetes mellitus, cancer. Standard blood analyses, high-sensitivity C-reactive protein (hsCRP), and fasting insulin were assessed. Insulin resistance was estimated using homeostasis model assessment of insulin resistance (HOMA-IR). The National Cholesterol Education Program Adult Treatment Panel-III definition of MetS was used. All subjects underwent brain magnetic resonance imaging (1.5 Tesla). Deep and periventricular WMHs were rated using the Fazekas scale on a 4-point scale, as described elsewhere.9 Depressive symptoms were rated using the 15-item Geriatric Depression Scale (GDS). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), whose details are described elsewhere.10 Statistical analyses were performed using SPSS (version 17.0 for Windows, SPSS, Inc., Chicago, IL). Multivariable regression models were constructed to identify independent predictors of higher GDS scores, controlling for age, sex, and education. Hs-CRP values were normalized using log10 transformation. Statistical significance was set for two-sided P-values <.05. Participants with depression were more likely to have MetS (82.3% vs 40.0%; P < .001), higher HOMA-IR (P = .004) and hsCRP (P = .006), more-severe deep and periventricular WMHs (P < .001), and lower visuospatial and executive MoCA subscale scores (P = .006) than controls. Average MoCA total scores were 24.1 ± 3.3 for cases and 24.5 ± 3.7 for controls (P = .715). Table 1 depicts the multivariable analyses. The greater number of depressive symptoms was no longer associated with poorer visuospatial and executive function after adjusting for WMH severity. Deep but not periventricular WMH severity was positively associated with depressive symptoms. Individuals with late-onset depression, even in the absence of overt cognitive impairment, had poorer executive and visuospatial function and more-severe WMHs than controls. Higher GDS scores were not associated with poorer visuospatial and executive function after adjustment for WMHs severity. Deep WMHs predicted a greater number of depressive symptoms. Cardio-metabolic risk factors lead in time to deep white matter changes, which may promote depression and loss of cognitive function as the result of the cross-talk between arterial and brain aging.4 The findings of the current study support the hypothesis that depression with onset in late life is associated with deterioration in domain-specific cognition because of their shared association with brain vascular injury. MetS mostly affects older adults, and it independently accelerates arterial aging and affects the risk of depression and cognitive disorders.4, 5 MetS predicts depression severity independent of WMHs, as well as blood glucose, inflammatory levels, and insulin resistance, indicating that MetS accelerates brain aging in addition to its association with WMHs. Further investigations are needed to better characterize the mechanisms through which cardio-metabolic disorders promote brain dysfunction in old age; longitudinal studies will reveal whether the early targeting of such risk factors may reduce the prevalence of depression and cognitive disorders at a population level. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Giovanni Viscogliosi reviewed the current medical literature, performed the statistical analyses, and wrote the letter. Evaristo Ettorre, Licia Manzon, and Paola Andreozzi collected participant data and performed the neuropsychological studies. Mauro Cacciafesta conceived of the letter. Sponsor's Role: None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".