Evaluation of Innotrac Aio! Second-Generation Cardiac Troponin I Assay: The Main Characteristics for Routine Clinical Use

The availability of a simple, sensitive, and rapid test using whole blood to facilitate processing and to reduce the turnaround time could improve the management of patients presenting with chest pain. The aim of this study was an evaluation of the Innotrac Aio! second-generation cardiac troponin I (cTnI) assay. The Innotrac Aio! second-generation cTnI assay was compared with the Abbott AxSYM first-generation cTnI, Beckman Access AccuTnI, and Innotrac Aio! first-generation cTnI assays. We studied serum samples from 15 patients with positive rheumatoid factor but with no indication of myocardial infarction (MI). Additionally, the stability of the sample with different matrices and the influence of hemodialysis on the cTnI concentration were evaluated. Within-assay CVs were 3.2%–10.9%, and between-assay precision ranged from 4.0% to 17.2% for cTnI. The functional sensitivity (CV = 20 %) and the concentration giving CV of 10% were approximated to be 0.02 and 0.04, respectively. The assay was found to be linear within the tested range of 0.063–111.6 μ g/L. The correlations between the second-generation Innotrac Aio!, Access, and AxSYM cTnI assays were good (r coefficients 0.947–0.966), but involved differences in the measured concentrations, and the biases were highest with cTnI at low concentrations. The second-generation Innotrac Aio! cTnI assay was found to be superior to the first-generation assay with regard to precision in the low concentration range. The stability of the cTnI level was best in the serum, lithium-heparin plasma, and lithium-heparin whole blood samples (n = 10 , decrease < 10 % in 24 hours at +20°C and at +4°C. There was no remarkable influence of hemodialysis on the cTnI release. False-positive cTnI values occurred in the presence of very high rheumatoid factor values, that is, over 3000 U/L. The 99th percentile of the apparently healthy reference group was ≤ 0.03   μ g/L. The results demonstrate the very good analytical performance of the second-generation Innotrac Aio! cTnI assay

Association between Birth Characteristics and Cardiovascular Autonomic Function at Mid-Life

Background Low birth weight is associated with an increased risk of cardiovascular diseases in adulthood. As abnormal cardiac autonomic function is a common feature in cardiovascular diseases, we tested the hypothesis that low birth weight may also be associated with poorer cardiac autonomic function in middle-aged subjects. Methods At the age of 46, the subjects of the Northern Finland Birth Cohort 1966 were invited to examinations including questionnaires about health status and life style and measurement of vagally-mediated heart rate variability (rMSSD) from R-R intervals (RRi) and spontaneous baroreflex sensitivity (BRS) in both seated and standing positions. Maternal parameters had been collected in 1965–1966 since the 16th gestational week and birth variables immediately after delivery. For rMSSD, 1,799 men and 2,279 women without cardiorespiratory diseases and diabetes were included and 902 men and 1,020 women for BRS. The analyses were adjusted for maternal (age, anthropometry, socioeconomics, parity, gestational smoking) and adult variables (life style, anthropometry, blood pressure, glycemic and lipid status) potentially confounding the relationship between birth weight and autonomic function. Results In men, birth weight correlated negatively with seated (r = -0.058, p = 0.014) and standing rMSSD (r = -0.090, p<0.001), as well as with standing BRS (r = -0.092, p = 0.006). These observations were verified using relevant birth weight categories (<2,500 g; 2,500–3,999 g; ≥4,000 g). In women, birth weight was positively correlated with seated BRS (r = 0.081, p = 0.010), but none of the other measures of cardiovascular autonomic function. These correlations remained significant after adjustment for potential confounders (p<0.05 for all). Conclusions In men, higher birth weight was independently associated with poorer cardiac autonomic function at mid-life. Same association was not observed in women. Our findings suggest that higher, not lower, birth weight in males may contribute to less favourable cardiovascular autonomic regulation and potentially to an elevated cardiovascular risk in later life

Development and validation of a model to predict incident chronic liver disease in the general population : The CLivD score

Background & Aims: Current screening strategies for chronic liver disease focus on detection of subclinical advanced liver fibrosis but cannot identify those at high future risk of severe liver disease. Our aim was to develop and validate a risk pre-diction model for incident chronic liver disease in the general population based on widely available factors. Methods: Multivariable Cox regression analyses were used to develop prediction models for liver-related outcomes with and without laboratory measures (Modellab and Modelnon-lab) in 25,760 individuals aged 40-70 years. Their data were sourced from the Finnish population-based health examination surveys FINRISK 1992-2012 and Health 2000 (derivation cohort). The models were externally validated in the Whitehall II (n = 5,058) and Copenhagen City Heart Study (CCHS) (n = 3,049) cohorts. Results: The absolute rate of incident liver outcomes per 100,000 person-years ranged from 53 to 144. The final prediction model included age, sex, alcohol use (drinks/week), waist-hip ratio, diabetes, and smoking, and Modellab also included gamma-glutamyltransferase values. Internally validated Wolbers' C -sta-tistics were 0.77 for Modellab and 0.75 for Modelnon-lab, while apparent 15-year AUCs were 0.84 (95% CI 0.75-0.93) and 0.82 (95% CI 0.74-0.91). The models identified a small proportion ( 10% absolute 15-year risk for liver events. Of all liver events, only 10% occurred in participants in the lowest risk category. In the validation cohorts, 15-year AUCs were 0.78 (Modellab) and 0.65 (Modelnon-lab) in the CCHS cohort, and 0.78 (Modelnon-lab) in the Whitehall II cohort. Conclusions: Based on widely available risk factors, the Chronic Liver Disease (CLivD) score can be used to predict risk of future advanced liver disease in the general population. Lay summary: Liver disease often progresses silently without symptoms and thus the diagnosis is often delayed until severe complications occur and prognosis becomes poor. In order to identify individuals in the general population who have a high risk of developing severe liver disease in the future, we developed and validated a Chronic Liver Disease (CLivD) risk prediction score, based on age, sex, alcohol use, waist-hip ratio, diabetes, and smoking, with or without measurement of the liver enzyme gamma-glutamyltransferase. The CLivD score can be used as part of health counseling, and for planning further liver investigations and follow-up. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver.Peer reviewe

Development and validation of a model to predict incident chronic liver disease in the general population: the CLivD score

BACKGROUND & AIMS: Current screening strategies for chronic liver disease focus on detection of subclinical advanced liver fibrosis but cannot identify persons at high future risk for severe liver disease. Our aim was to develop and validate a risk prediction model for incident chronic liver disease in the general population based on widely available factors. METHODS: Multivariable Cox regression analyses were used to develop prediction models for liver-related outcomes with and without laboratory measures (Modellab and Modelnon-lab) in 25,760 individuals aged 40-70 years. Their data were sourced from the Finnish population-based health examination surveys FINRISK 1992-2012 and Health 2000 (derivation cohort). The models were externally validated in the Whitehall II (n = 5058) and Copenhagen City Heart Study (CCHS) (n = 3049) cohorts. RESULTS: The absolute rate of incident liver outcomes per 100,000 person-years ranged from 53 to 144. The final prediction model included age, sex, alcohol use (drinks/week), waist-hip ratio, diabetes, and smoking, and Modellab also included gamma-glutamyltransferase values. Internally-validated Wolbers' C-statistics were 0.77 for Modellab and 0.75 for Modelnon-lab, while apparent 15-year AUCs were 0.84 (95% CI 0.75-0.93) and 0.82 (95% CI 0.74-0.91). The models identified a small proportion (10% absolute 15-year risk for liver events. Of all liver events, only 10% occurred in participants in the lowest risk category. In the validation cohorts, 15-year AUCs were 0.78 (Modellab) and 0.65 (Modelnon-lab) in the CCHS cohort, and 0.78 (Modelnon-lab) in the Whitehall II cohort. CONCLUSIONS: Based on widely available risk factors, this Chronic Liver Disease (CLivD) score can be used to predict risk for future advanced liver disease in the general population. LAY SUMMARY: Liver disease often progresses silently without symptoms and thus the diagnosis is often delayed until severe complications occur and prognosis becomes poor. In order to identify individuals in the general population who have high risk of developing severe liver disease in the future, we developed and validated a Chronic liver disease (CLivD) risk prediction score, based on age, sex, alcohol use, waist-hip ratio, diabetes, smoking, with or without gamma-glutamyltransferase (GGT). The CLivD score can be used as part of health counseling, and for planning further liver investigations and follow-up

Alcohol, metabolic risk and elevated serum gamma-glutamyl transferase (GGT) in Indigenous Australians

<p>Abstract</p> <p>Background</p> <p>The interaction between overweight/obesity and alcohol intake on liver enzyme concentrations have been demonstrated. No studies have yet examined the interaction between metabolic syndrome or multiple metabolic risk factors and alcohol intake on liver enzymes. The aim of this study was to examine if alcohol consumption modifies the effect of metabolic risk on elevated serum GGT in Indigenous Australians.</p> <p>Methods</p> <p>Data were from N = 2609 Indigenous Australians who participated in a health screening program in rural far north Queensland in 1999-2000 (44.5% response rate). The individual and interactive effects of metabolic risk and alcohol drinking on elevated serum GGT concentrations (≥50 U/L) were analyzed using logistic regression.</p> <p>Results</p> <p>Overall, 26% of the population had GGT≥50 U/L. Elevated GGT was associated with alcohol drinking (moderate drinking: OR 2.3 [95%CI 1.6 - 3.2]; risky drinking: OR 6.0 [4.4 - 8.2]), and with abdominal obesity (OR 3.7 [2.5 - 5.6]), adverse metabolic risk cluster profile (OR 3.4 [2.6 - 4.3]) and metabolic syndrome (OR 2.7 [2.1 - 3.5]) after adjustment for age, sex, ethnicity, smoking, physical activity and BMI. The associations of obesity and metabolic syndrome with elevated GGT were similar across alcohol drinking strata, but the association of an adverse metabolic risk cluster profile with elevated GGT was larger in risky drinkers (OR 4.9 [3.7 - 6.7]) than in moderate drinkers (OR 2.8 [1.6 - 4.9]) and abstainers (OR 1.6 [0.9 - 2.8]).</p> <p>Conclusions</p> <p>In this Indigenous population, an adverse metabolic profile conferred three times the risk of elevated GGT in risky drinkers compared with abstainers, independent of sex and ethnicity. Community interventions need to target both determinants of the population's metabolic status and alcohol consumption to reduce the risk of elevated GGT.</p

Anti-Mullerian hormone: correlation with testosterone and oligo- or amenorrhoea in female adolescence in a population-based cohort study

Study questions: Can serum anti-Müllerian hormone (AMH) levels measured in female adolescents predict polycystic ovary syndrome (PCOS)-associated features in adolescence and early adulthood? Summary answer: AMH levels associated well with PCOS-associated features (such as testosterone levels and oligoamenorrhoea) in adolescence, but was not an ideal marker to predict PCOS-associated features in early adulthood. What is known already: Several studies have reported that there is a strong correlation between antral follicle count and serum AMH levels and that women with PCOS/PCO have significantly higher serum AMH levels than women with normal ovaries. Other studies have reported an association between AMH serum levels and hyperandrogenism in adolescence, but none has prospectively assessed AMH as a risk predictor for developing features of PCOS during adulthood. Study design, size, duration: A subset of 400 girls was selected from the prospective population-based Northern Finland Birth Cohort 1986 (n = 4567 at age 16 and n = 4503 at age 26). The population has been followed from 1986 to the present. Participants/material, setting, methods: At age 16, 400 girls (100 from each testosterone quartile: 50 with oligo- or amenorrhoea and 50 with a normal menstrual cycle) were selected at random from the cohort for AMH measurement. Metabolic parameters were also assessed at age 16 in all participants. Postal questionnaires enquired about oligo- or amenorrhoea, hirsutism, contraceptive use and reproductive health at ages 16 and 26. Main results and role of chance: There was a significant correlation between AMH and testosterone at age 16 (r = 0.36, P < 0.001). AMH levels at age 16 were significantly higher among girls with oligo- or amenorrhoea compared with girls with normal menstrual cycles (35.9 pmol/l [95% CI: 33.2;38.6] versus 27.7 pmol/l [95% CI: 25.0;30.4], P < 0.001). AMH at age 16 was higher in girls who developed hirsutism at age 26 compared with the non-hirsute group (31.4 pmol/l [95% CI 27.1;36.5] versus 25.8 pmol/l [95% CI 23.3;28.6], P = 0.036). AMH at age 16 was also higher in women with PCOS at age 26 compared with the non-PCOS subjects (38.1 pmol/l [95% CI 29.1;48.4] versus 30.2 pmol/l [95% CI 27.9;32.4], P = 0.044). The sensitivity and specificity of the AMH (cut-off 22.5 pmol/l) for predicting PCOS at age 26 was 85.7 and 37.5%, respectively. The addition of testosterone did not significantly improve the accuracy of the test. There was no significant correlation between AMH levels and metabolic indices at age 16. Implications, reasons for cauntion: AMH is related to oligo- or amenorrhoea in adolescence, but it is not a good marker for metabolic factors. The relatively low rate of participation in the questionnaire at age 26 may also have affected the results. AMH was measured in a subset of the whole cohort. AMH measurement is lacking international standardization and therefore the concentrations and cut-off points are method dependent. Wider implications for the findings: Using a high enough cut-off value of AMH to predict which adolescents are likely to develop PCOS in adulthood could help to manage the condition from an early age due to a good sensitivity. However, because of its low specificity, it is not an ideal diagnostic marker, and its routine use in clinical practice cannot, at present, be recommended

Systematic evaluation of the association between hemoglobin levels and metabolic profile implicates beneficial effects of hypoxia

Copyright © 2021 The Authors. Activation of the hypoxia-inducible factor (HIF) pathway reprograms energy metabolism. Hemoglobin (Hb) is the main carrier of oxygen. Using its normal variation as a surrogate measure for hypoxia, we explored whether lower Hb levels could lead to healthier metabolic profiles in mice and humans (n = 7175) and used Mendelian randomization (MR) to evaluate potential causality (n = 173,480). The results showed evidence for lower Hb levels being associated with lower body mass index, better glucose tolerance and other metabolic profiles, lower inflammatory load, and blood pressure. Expression of the key HIF target genes SLC2A4 and Slc2a1 in skeletal muscle and adipose tissue, respectively, associated with systolic blood pressure in MR analyses and body weight, liver weight, and adiposity in mice. Last, manipulation of murine Hb levels mediated changes to key metabolic parameters. In conclusion, low-end normal Hb levels may be favorable for metabolic health involving mild chronic activation of the HIF response.Academy of Finland grants 266719 and 308009; S. Jusélius Foundation; Emil Aaltonen Foundation; Jane and Aatos Erkko Foundation; Academy of Finland Profi 5 funding for mathematics and AI: data insight for high-dimensional dynamics and the Academy of Finland Project 312123; British Heart Foundation Centre of Research Excellence (RE/18/4/34215); National Institute for Health Research Clinical Lectureship (CL-2020-16-001) at St. George’s, University of London. NFBC1966; University of Oulu grant nos. 65354 and 24000692; Oulu University Hospital grant nos. 2/97, 8/97, and 24301140; Ministry of Health and Social Affairs grant nos. 23/251/97, 160/97, and 190/97; National Institute for Health and Welfare, Helsinki grant no. 54121; Regional Institute of Occupational Health, Oulu, Finland grant nos. 50621 and 54231; ERDF European Regional Development Fund grant no. 539/2010 A31592; the European Union’s Horizon 2020 research and innovation programme grant agreement nos. 633595 (DynaHEALTH), 733206 (LifeCycle), 643774 (iHEALTH-T2D), 824989 (EUCAN Connect), and 721567 (EU H2020-MSCA-ITN-2016 CAPICE Marie Sklodowska-Curie), and grant nos. MR/M013138/1, MRC/BBSRC, and MR/S03658X/1 (the Medical Research Council, UK, JPI HDHL); and Academy of Finland, University Hospital Oulu, and NHLBI grant 5R01HL087679-02 through the STAMPEED program; The Young Finns Study has been financially supported by the Academy of Finland: grants 322098, 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere, and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; The Sigrid Juselius Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; and Diabetes Research Foundation of Finnish Diabetes Association; European Union’s Horizon 2020 research and innovation programme under grant agreement no. 848146; grant agreement 755320 for TAXINOMISIS; European Research Council (grant 742927 for MULTIEPIGEN project); and Tampere University Hospital Supporting Foundation and Finnish Society of Clinical Chemistry

Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts

Aims/hypothesis Metabolomics technologies have identified numerous blood biomarkers for type 2 diabetes risk in case−control studies of middle-aged and older individuals. We aimed to validate existing and identify novel metabolic biomarkers predictive of future diabetes in large cohorts of young adults. Methods NMR metabolomics was used to quantify 229 circulating metabolic measures in 11,896 individuals from four Finnish observational cohorts (baseline age 24–45 years). Associations between baseline metabolites and risk of developing diabetes during 8–15 years of follow-up (392 incident cases) were adjusted for sex, age, BMI and fasting glucose. Prospective metabolite associations were also tested with fasting glucose, 2 h glucose and HOMA-IR at follow-up. Results Out of 229 metabolic measures, 113 were associated with incident type 2 diabetes in meta-analysis of the four cohorts (ORs per 1 SD: 0.59–1.50; p< 0.0009). Among the strongest biomarkers of diabetes risk were branched-chain and aromatic amino acids (OR 1.31–1.33) and triacylglycerol within VLDL particles (OR 1.33–1.50), as well as linoleic n-6 fatty acid (OR 0.75) and non-esterified cholesterol in large HDL particles (OR 0.59). The metabolic biomarkers were more strongly associated with deterioration in post-load glucose and insulin resistance than with future fasting hyperglycaemia. A multi-metabolite score comprised of phenylalanine, non-esterified cholesterol in large HDL and the ratio of cholesteryl ester to total lipid in large VLDL was associated with future diabetes risk (OR 10.1 comparing individuals in upper vs lower fifth of the multi-metabolite score) in one of the cohorts (mean age 31 years). Conclusions/interpretation Metabolic biomarkers across multiple molecular pathways are already predictive of the long-term risk of diabetes in young adults. Comprehensive metabolic profiling may help to target preventive interventions for young asymptomatic individuals at increased risk

Do Mismatches between Pre- and Post-Natal Environments Influence Adult Physiological Functioning?

Purpose: Mismatches between pre- and post-natal environments have implications for disease in adulthood. However, less is known about how this mismatch can affect physiological systems more generally, especially at younger ages. We hypothesised that mismatches between pre- and post-natal environments, as measured by the measures of birthweight and adult leg length, would be associated with poorer biomarker levels across five key physiological systems in young adults. Methods: Data were collected from 923, 36 year-old respondents from the West of Scotland Twenty-07 Study. The biomarkers were: systolic blood pressure (sBP); forced expiratory volume (FEV1); glycated haemoglobin (HbA1c); glomerular filtration rate (eGFR); and gamma- glutamyltransferase (GGT). These biomarkers were regressed against pre-natal conditions (birthweight), post-natal conditions (leg length) and the interaction between pre- and post-natal measures. Sex, childhood socioeconomic position and adult lifestyle characteristics were adjusted for as potential effect modifiers and confounders, respectively. Results: There were no associations between birthweight and leg length and sBP, FEV1, HbA1c, or GGT. Higher birthweight and longer leg length were associated with better kidney function (eGFR). However, there was no evidence for mismatches between birthweight and leg length to be associated with worse sBP, FEV1, HbA1c, eGFR or GGT levels (P>0.05). Conclusions: Our hypothesis that early signs of physiological damage would be present in young adults given mismatches in childhood environments, as measured by growth markers, was not proven. This lack of association could be because age 36 is too young to identify significant trends for future health, or the associations simply not being present. © 2014 Robertson, Benzeval