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1.
Lancet ; 402(10406): 988-996, 2023 09 16.
Article in English | MEDLINE | ID: mdl-37572680

ABSTRACT

BACKGROUND: Liver cirrhosis is a major cause of death worldwide. Cirrhosis develops after a long asymptomatic period of fibrosis progression, with the diagnosis frequently occurring late, when major complications or cancer develop. Few reliable tools exist for timely identification of individuals at risk of cirrhosis to allow for early intervention. We aimed to develop a novel score to identify individuals at risk for future liver-related outcomes. METHODS: We derived the LiverRisk score from an international prospective cohort of individuals from six countries without known liver disease from the general population, who underwent liver fibrosis assessment by transient elastography. The score included age, sex, and six standard laboratory variables. We created four groups: minimal risk, low risk, medium risk, and high risk according to selected cutoff values of the LiverRisk score (6, 10, and 15). The model's discriminatory accuracy and calibration were externally validated in two prospective cohorts from the general population. Moreover, we ascertained the prognostic value of the score in the prediction of liver-related outcomes in participants without known liver disease with median follow-up of 12 years (UK Biobank cohort). FINDINGS: We included 14 726 participants: 6357 (43·2%) in the derivation cohort, 4370 (29·7%) in the first external validation cohort, and 3999 (27·2%) in the second external validation cohort. The score accurately predicted liver stiffness in the development and external validation cohorts, and was superior to conventional serum biomarkers of fibrosis, as measured by area under the receiver-operating characteristics curve (AUC; 0·83 [95% CI [0·78-0·89]) versus the fibrosis-4 index (FIB-4; 0·68 [0·61-0·75] at 10 kPa). The score was effective in identifying individuals at risk of liver-related mortality, liver-related hospitalisation, and liver cancer, thereby allowing stratification to different risk groups for liver-related outcomes. The hazard ratio for liver-related mortality in the high-risk group was 471 (95% CI 347-641) compared with the minimal risk group, and the overall AUC of the score in predicting 10-year liver-related mortality was 0·90 (0·88-0·91) versus 0.84 (0·82-0·86) for FIB-4. INTERPRETATION: The LiverRisk score, based on simple parameters, predicted liver fibrosis and future development of liver-related outcomes in the general population. The score might allow for stratification of individuals according to liver risk and thus guide preventive care. FUNDING: European Commission under the H20/20 programme; Fondo de Investigación Sanitaria de Salud; Instituto de Salud Carlos III; Spanish Ministry of Economy, Industry, and Competitiveness; the European Regional Development Fund; and the German Ministry of Education and Research (BMBF).


Subject(s)
Liver Cirrhosis , Humans , Prognosis , Prospective Studies , Liver Cirrhosis/diagnosis , Liver Cirrhosis/epidemiology , Risk Factors , Fibrosis
2.
Chronobiol Int ; 39(11): 1508-1516, 2022 Nov.
Article in English | MEDLINE | ID: mdl-36210507

ABSTRACT

Mechanistic studies are needed to understand how rotating shift work perturbs metabolic processing. We collected plasma samples (n = 196) from 49 males, rotating car factory shift workers at the beginning and end of a night-shift (22:00-06:00 h) and day-shift (06:00 h-14:00 h). Samples underwent targeted LC-MS/MS metabolomics and concentrations of 130 metabolites were log2-transformed and pareto-scaled. An elastic net selected the most influential metabolites for linear mixed models examining within-person variation in metabolite levels at night-shift end (06:00 h) compared to day-shift start (06:00 h). Quantitative enrichment analysis explored differentially enriched biological pathways between sample time points. We included 20 metabolites (amino acids, biogenic amines, acylcarnitines, glycerophospholipids) in mixed models. Night-shift was associated with changes in concentrations of arginine (geometric mean ratio [GMR] 2.30, 95%CI 1.25, 4.23), glutamine (GMR 2.22, 95%CI 1.53, 3.24), kynurenine (GMR 3.22, 95%CI 1.05, 9.87), lysoPC18:2 (GMR 1.86, 95%CI 1.11, 3.11), lysoPC20:3 (GMR 2.48, 95%CI 1.05, 5.83), PCaa34:2 (GMR 2.27, 95%CI 1.16, 4.44), and PCae38:5 (GMR 1.66, 95%CI 1.02, 2.68). Tryptophan metabolism, glutathione metabolism, alanine metabolism, glycine and serine metabolism, and urea cycle were pathways differing between shifts. Night shift work was associated with changes in metabolites and the perturbation of metabolic and biochemical pathways related to a variety of health outcomes.


Subject(s)
Shift Work Schedule , Male , Humans , Circadian Rhythm , Chromatography, Liquid , Tandem Mass Spectrometry , Linear Models , Work Schedule Tolerance
3.
Scand J Work Environ Health ; 48(1): 41-51, 2022 01 01.
Article in English | MEDLINE | ID: mdl-34623452

ABSTRACT

OBJECTIVE: Data from real world settings on circadian disruption and subsequent hormone-related changes may explain the higher risk of hormone-dependent cancers among night shift workers.The present study examines the melatonin and sex steroid hormone levels among night shift workers. METHODS: We included 44 male, rotating shift workers from a car factory in Spain, sampled both at the end of a 3-week night shift (22:00-06:00 hrs) and a 3-week early morning shift (06:00-14:00 hrs). Participants collected all urine voids over 24-hours during each shift. Urinary concentrations of sex steroid hormones (estrogens, androgens and progestogens) and 6-sulfatoxymelatonin (aMT6s, major melatonin metabolite) were determined. Individual cosinor analysis was used to derive the acrophase (peak time) and area under the curve (total production). Linear mixed models examined intraindividual associations between night shift work and log-transformed 24-hour peak time and total production of hormones compared to early morning shift work. RESULTS: The acrophase was delayed during the night shift for aMT6s [geometric mean difference (GMD) 7.53 hrs, 95% confidence interval (CI) 4.46-10.60], androgens (eg, testosterone: GMD 6.83 hrs, 95% CI 0.34-13.32) and progestogens (eg, 17-hydroxyprogesterone: GMD 4.54 hrs, 95% CI 2.92-6.16) compared to the early morning shift. We found a higher production of adrenal androgen 11-oxoandrosterone/11-oxoetiocholanolone [geometric mean ratio (GMR) 1.43, 95% CI 1.12-1.81], and a lower production of adrenal progestogen 16-cysteinylprogesterone (GMR 0.79, 95% CI 0.67-0.93) during the night shift compared to the early morning shift levels. CONCLUSIONS: Night shift work was associated with melatonin and sex hormone-related changes in timing and total production, providing insight into the mechanistic path for its association with hormone-dependent cancer.


Subject(s)
Melatonin , Shift Work Schedule , Circadian Rhythm , Gonadal Steroid Hormones , Humans , Male , Melatonin/metabolism , Work Schedule Tolerance
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