The purpose of this study was to assess whether increasing serum uric acid (UA) levels are related to cardiovascular disease (CVD) mortality, all-cause mortality, and incident (fatal and nonfatal) ...myocardial infarction (MI) in men from the general population taking into account C-reactive protein (CRP), a sensitive marker of systemic inflammation.
The study was based on 3604 men (45 to 74 years of age) who participated in 1 of the 3 MONICA Augsburg surveys between 1984 and 1995. All participants were prospectively followed within the framework of the Cooperative Health Research in the Region of Augsburg (KORA). Up to December 31, 2002, there occurred 809 total deaths, 359 CVD deaths, and 297 incident MIs. In a Cox model, comparing extreme quartiles of the UA distribution, the hazard ratio for CVD mortality was 1.44 (95% confidence interval CI 1.04 to 2.0), and for all-cause mortality it was 1.40 (95% CI 1.13 to 1.74) after adjustment for conventional cardiovascular risk factors, CRP, and diuretic intake. However, UA was not associated with incident MI after multivariable adjustment.
High UA levels were independently associated with CVD mortality as well as all-cause mortality but not with incident MI in middle-aged men from the general population.
Multimorbidity is a common problem in aged populations with a wide range of individual and societal consequences. The objective of the study was to explore patterns of comorbidity and multimorbidity ...in an elderly population using different analytical approaches. Data were gathered from the population-based KORA-Age project, which included 4,127 persons aged 65-94 years living in the city of Augsburg and its two surrounding counties in Southern Germany. Information on the presence of 13 chronic conditions was collected in a standardized telephone interview and a self-administered questionnaire. Patterns of comorbidity and multimorbidity were analyzed using prevalence figures, logistic regression models and exploratory tetrachoric factor analysis. The prevalence of multimorbidity (≥2 diseases) was 58.6% in the total sample. Hypertension and diabetes (Odds Ratio OR 2.95, 99.58% confidence interval CI 2.19-3.96), as well as hypertension and stroke (OR 2.00, 99.58% CI 1.26-3.16) most often occurred in combination. This association was independent of age, sex and the presence of other conditions. Using factor analysis, we identified four patterns of multimorbidity: the first pattern includes cardiovascular and metabolic diseases, the second includes joint, liver, lung and eye diseases, the third covers mental and neurologic diseases and the fourth pattern includes gastrointestinal diseases and cancer. 44% of the persons were assigned to at least one of the four multimorbidity patterns; 14% could be assigned to both the cardiovascular/metabolic and the joint/liver/lung/eye pattern. Further common pairs were the mental/neurologic pattern combined with the cardiovascular/metabolic pattern (7.2%) or the joint/liver/lung/eye pattern (5.3%), respectively. Our results confirmed the existence of co-occurrence of certain diseases in elderly persons, which is not caused by chance. Some of the identified patterns of multimorbidity and their overlap may indicate common underlying pathological mechanisms.
Health-related quality of life (HRQL) has become an increasingly important outcome parameter in clinical trials and epidemiological research. HRQL scores are typically bounded at both ends of the ...scale and often highly skewed. Several regression techniques have been proposed to model such data in cross-sectional studies, however, methods applicable in longitudinal research are less well researched. This study examined the use of beta regression models for analyzing longitudinal HRQL data using two empirical examples with distributional features typically encountered in practice.
We used SF-6D utility data from a German older age cohort study and stroke-specific HRQL data from a randomized controlled trial. We described the conceptual differences between mixed and marginal beta regression models and compared both models to the commonly used linear mixed model in terms of overall fit and predictive accuracy.
At any measurement time, the beta distribution fitted the SF-6D utility data and stroke-specific HRQL data better than the normal distribution. The mixed beta model showed better likelihood-based fit statistics than the linear mixed model and respected the boundedness of the outcome variable. However, it tended to underestimate the true mean at the upper part of the distribution. Adjusted group means from marginal beta model and linear mixed model were nearly identical but differences could be observed with respect to standard errors.
Understanding the conceptual differences between mixed and marginal beta regression models is important for their proper use in the analysis of longitudinal HRQL data. Beta regression fits the typical distribution of HRQL data better than linear mixed models, however, if focus is on estimating group mean scores rather than making individual predictions, the two methods might not differ substantially.
Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire ...spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.
40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).
Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.
To examine gender-specific associations between sleep duration and sleep complaints and incident myocardial infarction (MI).
Cohort study.
A representative population sample of middle-aged subjects ...in Germany.
The study was based on 3508 men and 3388 women (aged 45 to 74 years) who participated in one of the 3 MONICA (Monitoring trends and determinants on cardiovascular diseases) Augsburg surveys between 1984 and 1995, who were free of MI and angina pectoris at baseline and were followed up until 2002.
N/A.
A total of 295 cases of incident MI among men and 85 among women occurred during a mean follow-up period of 10.1 years. Compared with women sleeping 8 hours, the multivariable adjusted hazard ratio (HR) of MI among women sleeping < or =5 hours was 2.98 (95% CI, 1.48-6.03), and among women sleeping > or =9 hours 1.40 (95% CI, 0.74-2.64); the corresponding HRs among men were 1.13 (95% CI, 0.66-1.92) and 1.07 (95% CI, 0.75-1.53). In multivariable analysis the relative risk of an incident MI for men and women with difficulties maintaining sleep was 1.12 (95% CI, 0.84-1.48) and 1.53 (95% CI, 0.99-2.37), respectively, and for men and women with difficulties initiating sleep the relative risk was 1.16 (95% CI, 0.82-1.63) and 1.30 (95% CI, 0.81-2.06), respectively.
Modest associations between short sleep duration and difficulties maintaining sleep and incident MI were seen in middle-aged women but not men from the general population.
BACKGROUND: It remains controversial whether body mass index (BMI), waist circumference (WC), or waist-hip ratio (WHR) is a better risk predictor of type 2 diabetes. OBJECTIVE: The objective was to ...examine the sex-specific relevance of WC, WHR, and BMI to the development of type 2 diabetes. DESIGN: The prospective population-based cohort study was based on 3055 men and 2957 women aged 35-74 y who participated in the second (1989-1990) or third (1994-1995) MONICA (Monitoring Trends and Determinants on Cardiovascular Diseases) Augsburg survey. The subjects were free of diabetes at baseline. Hazard ratios (HRs) were estimated from Cox proportional hazards models. RESULTS: During a mean follow-up of 9.2 y, 243 cases of incident type 2 diabetes occurred in men and 158 occurred in women. Multivariable-adjusted HRs across quartiles of BMI were 1.0, 1.37, 2.08, and 4.15 in men and 1.0, 3.77, 4.95, and 10.58 in women; those of WC were 1.0, 1.15, 1.57, and 3.40 in men and 1.0, 3.21, 3.98, and 10.70 in women; those of WHR were 1.0, 1.14, 1.80, and 2.84 in men and 1.0, 0.82, 2.06, and 3.51 in women. In joint analyses, the highest risk was observed in men and women with a high BMI in combination with a high WC and a high WHR. CONCLUSIONS: Both overall and abdominal adiposity were strongly related to the development of type 2 diabetes. Because there was an additive effect of overall and abdominal obesity on risk prediction, WC should be measured in addition to BMI to assess the risk of type 2 diabetes in both sexes.
The metabolic syndrome is a major public health challenge and identifies persons at risk for diabetes and cardiovascular disease. The aim of this study was to examine the association between age at ...menarche and the metabolic syndrome (IDF and NCEP ATP III classification) and its components.
1536 women aged 32 to 81 years of the German population based KORA F4 study were investigated. Data was collected by standardized interviews, physical examinations, and whole blood and serum measurements.
Young age at menarche was significantly associated with elevated body mass index (BMI), greater waist circumference, higher fasting glucose levels, and 2 hour glucose (oral glucose tolerance test), even after adjusting for the difference between current BMI and BMI at age 25. The significant effect on elevated triglycerides and systolic blood pressure was attenuated after adjustment for the BMI change. Age at menarche was inversely associated with the metabolic syndrome adjusting for age (p-values: <0.001 IDF, 0.003 NCEP classification) and additional potential confounders including lifestyle and reproductive history factors (p-values: 0.001, 0.005). Associations remain significant when additionally controlling for recollected BMI at age 25 (p-values: 0.008, 0.033) or the BMI change since age 25 (p-values: 0.005, 0.022).
Young age at menarche might play a role in the development of the metabolic syndrome. This association is only partially mediated by weight gain and increased BMI. A history of early menarche may help to identify women at risk for the metabolic syndrome.
Background Metabolites such as creatinine and urea are established kidney function markers. High-throughput metabolomic studies have not been reported in large general population samples spanning ...normal kidney function and chronic kidney disease (CKD). Study Design Cross-sectional observational studies of the general population. Setting & Participants 2 independent samples: KORA F4 (discovery sample, n = 3,011) and TwinsUK (validation sample, n = 984). Exposure Factors 151 serum metabolites, quantified by targeted mass spectrometry. Outcomes & Measurements Metabolites and their 22,650 ratios were analyzed by multivariable-adjusted linear regression for their association with glomerular filtration rate (eGFR), estimated separately from creatinine and cystatin C levels by CKD-EPI (CKD Epidemiology Collaboration) equations. After correction for multiple testing, significant metabolites ( P < 3.3 × 10−4 for single metabolites; P < 2.2 × 10−6 for ratios) were meta-analyzed with independent data from the TwinsUK Study. Results Replicated associations with eGFR were observed for 22 metabolites and 516 metabolite ratios. Pooled P values ranged from 7.1 × 10−7 to 1.8 × 10−69 for the replicated single metabolites. Acylcarnitines such as glutarylcarnitine were associated inversely with eGFR (−3.73 mL/min/1.73 m2 per standard deviation SD increase, pooled P = 1.8 × 10−69 ). The replicated ratio with the strongest association was the ratio of serine to glutarylcarnitine ( P = 3.6 × 10−81 ). Almost all replicated phenotypes associated with decreased eGFR (<60 mL/min/1.73 m2 ; n = 172 cases) in KORA F4: per 1-SD increment, ORs ranged from 0.29-2.06. Across categories of a metabolic score consisting of 3 uncorrelated metabolites, the prevalence of decreased eGFR increased from 3% to 53%. Limitations Cross-sectional study design, GFR was estimated, limited number of metabolites. Conclusions Distinct metabolic phenotypes were reproducibly associated with eGFR in 2 separate population studies. They may provide novel insights into renal metabolite handling, improve understanding of pathophysiology, or aid in the diagnosis of kidney disease. Longitudinal studies are needed to clarify whether changes in metabolic phenotypes precede or result from kidney function impairment.
To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) S4 ...study.
Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs).
We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75 × 10(-16)-8.95 × 10(-06)) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism.
A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network.
PDF
