Abdominal aortic calcification on lateral spine images captured during bone density testing and late-life dementia risk in older women: A prospective cohort study

BACKGROUND

Dementia after the age of 80 years (late-life) is increasingly common due to vascular and non-vascular risk factors. Identifying individuals at higher risk of late-life dementia remains a global priority.

METHODS

In prospective study of 958 ambulant community-dwelling older women (≥70 years), lateral spine images (LSI) captured in 1998 (baseline) from a bone density machine were used to assess abdominal aortic calcification (AAC). AAC was classified into established categories (low, moderate and extensive). Cardiovascular risk factors and apolipoprotein E (APOE) genotyping were evaluated. Incident 14.5-year late-life dementia was identified from linked hospital and mortality records.

FINDINGS

At baseline women were 75.0 ± 2.6 years, 44.7% had low AAC, 36.4% had moderate AAC and 18.9% had extensive AAC. Over 14.5- years, 150 (15.7%) women had a late-life dementia hospitalisation (n = 132) and/or death (n = 58). Compared to those with low AAC, women with moderate and extensive AAC were more likely to suffer late-life dementia hospitalisations (9.3%, 15.5%, 18.3%, respectively) and deaths (2.8%, 8.3%, 9.4%, respectively). After adjustment for cardiovascular risk factors and APOE, women with moderate and extensive AAC had twice the relative hazards of late-life dementia (moderate, aHR 2.03 95%CI 1.38-2.97; extensive, aHR 2.10 95%CI 1.33-3.32), compared to women with low AAC.

INTERPRETATION

In community-dwelling older women, those with more advanced AAC had higher risk of late-life dementia, independent of cardiovascular risk factors and APOE genotype. Given the widespread use of bone density testing, simultaneously capturing AAC information may be a novel, non-invasive, scalable approach to identify older women at risk of late-life dementia.

FUNDING

Kidney Health Australia, Healthway Health Promotion Foundation of Western Australia, Sir Charles Gairdner Hospital Research Advisory Committee Grant, National Health and Medical Research Council of Australia.

Association of gamma-glutamyltransferase levels with total mortality, liver-related and cardiovascular outcomes: A prospective cohort study in the UK Biobank

BACKGROUND

Gamma-glutamyltransferase (GGT) levels in the blood can be a sensitive marker of liver injury but the extent to which they give insight into risk across multiple outcomes in a clinically useful way remains uncertain.

METHODS

Using data from 293,667 UK Biobank participants, the relationship of GGT concentrations to self-reported alcohol intake and adiposity markers were investigated. We next investigated whether GGT predicted liver-related, cardiovascular (CV) or all-cause mortality, and potentially improved CV risk prediction.

FINDINGS

Higher alcohol intake and greater waist circumference (WC) were associated with higher GGT; the association was stronger for alcohol with evidence of a synergistic effect of WC. Higher GGT concentrations were associated with multiple outcomes. Compared to a GGT of 14.5 U/L (lowest decile), values of 48 U/L for women and 60 U/L for men (common upper limits of 'normal') had hazard ratios (HRs) for liver-related mortality of 1.83 (95% CI 1.60-2.11) and 3.25 (95% CI 2.38-4.42) respectively, for CV mortality of 1.21 (95% CI 1.14-1.28) and 1.43 (95% CI 1.27-1.60) and for all-cause mortality of 1.15 (95% CI 1.12-1.18) and 1.31 (95% CI 1.24-1.38). Adding GGT to a risk algorithm for CV mortality reclassified an additional 1.24% (95% CI 0.14-2.34) of participants across a binary 5% 10-year risk threshold.

INTERPRETATION

Our study suggests that a modest elevation in GGT levels should trigger a discussion with the individual to review diet and lifestyle including alcohol intake and consideration of formal liver disease and CV risk assessment if not previously done.

FUNDING

British Heart Foundation Centre of Research Excellence Grant (grant number RE/18/6/34217), NHS Research Scotland (grant number SCAF/15/02), the Medical Research Council (grant number MC_UU_00022/2); and the Scottish Government Chief Scientist Office (grant number SPHSU17).

Multi-task deep learning network to predict future macrovascular invasion in hepatocellular carcinoma

BACKGROUND

Models predicting future macrovascular invasion in hepatocellular carcinoma are constructed to assist timely interventions.

METHODS

A total of 366 HCC cases were retrospectively collected from five Chinese hospitals between April 2007 and November 2016: the training dataset comprised 281 patients from four hospitals; the external validation dataset comprised 85 patients from another hospital. Multi-task deep learning network-based models were constructed to predict future macrovascular invasion. The discrimination, calibration, and decision curves were compared to identify the best model. We compared the time to macrovascular invasion and overall survival using the best model and related image heterogeneity scores (H-score). Then, we determined the need for a segmentation subnet or the replacement deep learning algorithm by logistic regression in screening clinical/radiological factors. Finally, an applet was constructed for future application.

FINDINGS

The best model combined clinical/radiological factors and radiomic features. It achieved best discrimination (areas under the curve: 0·877 in the training dataset and 0·836 in the validation dataset), calibration, and decision curve. Its performance was not affected by the treatments and disease stages. The subgroups had statistical significance for time to macrovascular invasion (training: hazard ratio [HR] = 0·073, 95% confidence interval [CI]: 0·032-0·167, p < 0·001 and validation: HR = 0·090, 95%CI: 0·022-0·366, p < 0·001) and overall survival (training: HR = 0·344, 95%CI: 0·246-0·547, p < 0·001 and validation: HR = 0·489, 95%CI: 0·279 - 0·859, p = 0·003). Similar results were achieved when the patients were subdivided by the H-score. The subnet for segmentation and end-to-end deep learning algorithms improved the performance of the model.

INTERPRETATION

Our multi-task deep learning network-based model successfully predicted future macrovascular invasion. In high-risk populations, besides the current first-line treatments, more therapies may be explored for macrovascular invasion.

Body composition predicts mortality and decompensation in compensated cirrhosis patients: A prospective cohort study

Background & Aims

Body composition, particularly sarcopenia, is associated with mortality in patients with decompensated cirrhosis undergoing transplant evaluation. Similar data are limited for non-transplant eligible or compensated patients.

Methods

A total of 274 patients with cirrhosis were followed prospectively for ≤5 years after a CT scan. We utilized Analytic Morphomics® to measure body composition (fat, muscle, and bone) which was rendered into relative values (percentiles) in relation to a reference population. The model for end-stage liver disease (MELD) score was used as a reference model for survival prediction. We validated our models in a separate cohort.

Results

Our cohort had a mean Child-Pugh score of 7.0 and a mean MELD of 11.3. The median follow-up time was 5.05 years. The proportion of patients alive at 1, 3 and 5 years was 86.5%, 68.0%, and 54.3%; 13 (4.6%) underwent liver transplantation. Child-Pugh B/C (vs. A) cirrhosis was associated with decreased muscle, subcutaneous, and visceral fat area but increased subcutaneous/visceral fat density. Decreased normal density muscle mass was associated with mortality (hazard ratio [HR] 0.984, p <0.001) as well as visceral and subcutaneous fat density (HR 1.013 and 1.014, respectively, p <0.001). Models utilizing these features outperformed MELD alone for mortality discrimination in both the derivation and validation cohort, particularly for those with compensated cirrhosis (C-statistics of 0.74 vs. 0.58). Using competing risk analysis, we found that subcutaneous fat density was most predictive of decompensation (subdistribution HR 1.018, p = 0.0001).

Conclusion

The addition of body composition features to predictive models improves the prospective determination of prognosis in patients with cirrhosis, particularly those with compensated disease. Fat density, a novel feature, is associated with the risk of decompensation.

Lay summary

Am I at high risk of getting sicker and dying? This is the key question on the mind of patients with cirrhosis. The problem is that we have very few tools to help guide our patients, particularly if they have early cirrhosis (without symptoms like confusion or fluid in the belly). We found that how much muscle and fat the patient has and what that muscle or fat looks like on a CT scan provide helpful information. This is important because many patients have CT scans and this information is hiding in plain sight.

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Features of body composition can predict clinical outcomes in patients with cirrhosis awaiting liver transplantation.

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Data are lacking regarding long-term outcomes among patients with compensated disease.

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We show that features of muscle and fat are associated with decompensation and risk of death across the spectrum of cirrhosis.

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CT scans obtained for unrelated clinical purposes can be analyzed as a digital risk biomarker for patients with compensated cirrhosis.