Effect of obesity on the associations of 25-hydroxyvitamin D with prevalent and incident distal sensorimotor polyneuropathy: population-based KORA F4/FF4 study.

BACKGROUND/OBJECTIVES: The association between vitamin D and DSPN has been investigated in cross-sectional studies in individuals with diabetes. However, evidence from prospective and population-based studies is still lacking. Also, the potential modifying effect of obesity and glucose tolerance has not been investigated. Therefore, we examined the cross-sectional and prospective associations of serum 25(OH)D with DSPN and assessed possible effect modifications. SUBJECTS/METHODS: The study included individuals aged 62-81 years who participated in the German KORA F4 (2006-2008) and FF4 (2013-2014) studies. DSPN was assessed using the Michigan Neuropathy Screening Instrument. Cross-sectional analyses (n = 1065; 33% of the participants had obesity) assessed the associations of baseline 25(OH)D with prevalent DSPN, while prospective analyses (n = 422) assessed the associations of 25(OH)D with incident DSPN. RESULTS: No association was found between 25(OH)D and prevalent DSPN in the total sample after adjustment for age, sex, season of blood sampling, BMI, metabolic variables, lifestyle factors, and comorbidities. However, a decrease by 10 nmol/L in 25(OH)D was associated with prevalent DSPN (RR (95% CI) 1.08 (1.01, 1.16)) in individuals with obesity but not in normal-weight individuals (RR (95% CI) 0.97 (0.92, 1.02), pinteraction = 0.002). No evidence for effect modification by glucose tolerance was found (p > 0.05). In the prospective analysis, 25(OH)D levels in the first and second tertiles were associated with higher risk of DSPN (RR (95% CI) 1.18 (1.02; 1.38) and 1.40 (1.04; 1.90)) compared to the third tertile after adjustment for age, sex, season of blood sampling, and BMI. There was no evidence for effect modification by obesity or glucose tolerance categories. CONCLUSIONS: Our study did not show consistent evidence for cross-sectional and prospective associations between serum 25(OH)D levels and DSPN in the total study population of older individuals. However, there was evidence for an association between lower serum 25(OH)D levels and higher prevalence of DSPN in individuals with obesity.

Novel biomarkers of inflammation, kidney function and chronic kidney disease in the general population.

BACKGROUND: Inflammatory processes have been implicated in the development of chronic kidney disease (CKD). We investigated the association of a large panel of inflammatory biomarkers reflecting aspects of immunity with kidney function and CKD incidence. METHODS: We used data from two independent population-based studies, KORA F4 (discovery, n = 1110, mean age 70.3 years, 48.7% male) and ESTHER (replication, n = 1672, mean age 61.9 years, 43.6% male). Serum levels of biomarkers were measured using proximity extension assay technology. The association of biomarkers with estimated glomerular filtration rate (eGFR) at baseline and with incident CKD was investigated using linear and logistic regression models adjusted for cardiorenal risk factors. Independent results from prospective analyses of both studies were pooled. The significance level was corrected for multiple testing by false-discovery rate (PFDR < 0.05). RESULTS: In the KORA F4 discovery study, 52 of 71 inflammatory biomarkers were inversely associated with eGFR based on serum creatinine. Top biomarkers included CD40, TNFRSF9 and IL10RB. Forty-two of these 52 biomarkers were replicated in the ESTHER study. Nine of the 42 biomarkers were associated with incident CKD independent of cardiorenal risk factors in the meta-analysis of the KORA (n = 142, mean follow-up 6.5 years) and ESTHER (n = 103, mean follow-up 8 years) studies. Pathway analysis revealed the involvement of inflammatory and immunomodulatory processes reflecting cross-communication of innate and adaptive immune cells. CONCLUSIONS: Novel and known biomarkers of inflammation were reproducibly associated with kidney function. Future studies should investigate their clinical utility and underlying molecular mechanisms in independent cohorts.

Influence of geographical latitude on vitamin D status: cross-sectional results from the BiomarCaRE consortium.

Even though sunlight is viewed as the most important determinant of 25-hydroxyvitamin D (25(OH)D) status, several European studies have observed higher 25(OH)D concentrations among north-Europeans than south-Europeans. We studied the association between geographical latitude (derived from ecological data) and 25(OH)D status in six European countries using harmonised immunoassay data from 81 084 participants in the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project (male sex 48·9 %; median age 50·8 years; examination period 1984-2014). Quantile regression models, adjusted for age, sex, decade and calendar week of sampling and time from sampling to analysis, were used for between-country comparisons. Up until the median percentile, the ordering of countries by 25(OH)D status (from highest to lowest) was as follows: Sweden (at 65·6-63·8°N), Germany (at 48·4°N), Finland (at 65·0-60·2°N), Italy (at 45·6-41·5°N), Scotland (at 58·2-55·1°N) and Spain (at 41·5°N). From the 75th percentile and upwards, Finland had higher values than Germany. As an example, using the Swedish cohort as a comparator, the median 25(OH)D concentration was 3·03, 3·28, 5·41, 6·54 and 9·28 ng/ml lower in the German, Finnish, Italian, Scottish and Spanish cohort, respectively (P-value < 0·001 for all comparisons). The ordering of countries was highly consistent in subgroup analyses by sex, age, and decade and season of sampling. In conclusion, we confirmed the previous observation of a north-to-south gradient of 25(OH)D status in Europe, with higher percentile values among north-Europeans than south-Europeans.

Serum uromodulin is inversely associated with biomarkers of subclinical inflammation in the population-based KORA F4 study.

BACKGROUND: Uromodulin is a kidney-specific glycoprotein synthesized in tubular cells of Henle's loop exerting nephroprotective and immunomodulatory functions in the urinary tract. A small amount of uromodulin is also released into the systemic circulation, where its physiological role is unknown. Serum uromodulin (sUmod) has been associated with metabolic risk factors and with cardiovascular events and mortality, where these associations were partly stronger in men than in women. In this study, we investigated the associations of sUmod with biomarkers of subclinical inflammation in a population-based sample of women and men. METHODS: Associations of sUmod with 10 biomarkers of subclinical inflammation were assessed in 1065 participants of the Cooperative Health Research in the Region of Augsburg (KORA) F4 study aged 62-81 years using linear regression models adjusted for sex, age, body mass index, estimated glomerular filtration rate and diabetes. Analyses were performed in the total study sample and stratified by sex. RESULTS: sUmod was inversely associated with white blood cell count, high-sensitive C-reactive protein, interleukin (IL)-6, tumour necrosis factor-α, myeloperoxidase, superoxide dismutase-3, IL-1 receptor antagonist and IL-22 after multivariable adjustment and correction for multiple testing (P < 0.001 for each observation). There was a trend towards a stronger association of sUmod with pro-inflammatory markers in men than in women, with a significant P for sex interaction (<0.001) regarding the relation of sUmod with IL-6. CONCLUSIONS: sUmod was inversely associated with biomarkers of subclinical inflammation in older participants of the KORA F4 study. The association of sUmod with IL-6 differed between women and men. Future research should focus on whether the immunomodulatory properties of sUmod are one explanation for the association of sUmod with cardiovascular outcomes and mortality.

Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study.

It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo ); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PLMetabo ); validation of two comprehensive polygenic risk scores: GRSGola based on Metabochip data, and GRSKhera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDIrel ) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRSMetabo and PLMetabo , GRSGola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDIrel : 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRSKhera yielded slightly worse prediction results than GRSGola .

Metabolic syndrome and the plasma proteome: from association to causation.

BACKGROUND: The metabolic syndrome (MetS), defined by the simultaneous clustering of cardio-metabolic risk factors, is a significant worldwide public health burden with an estimated 25% prevalence worldwide. The pathogenesis of MetS is not entirely clear and the use of molecular level data could help uncover common pathogenic pathways behind the observed clustering. METHODS: Using a highly multiplexed aptamer-based affinity proteomics platform, we examined associations between plasma proteins and prevalent and incident MetS in the KORA cohort (n = 998) and replicated our results for prevalent MetS in the HUNT3 study (n = 923). We applied logistic regression models adjusted for age, sex, smoking status, and physical activity. We used the bootstrap ranking algorithm of least absolute shrinkage and selection operator (LASSO) to select a predictive model from the incident MetS associated proteins and used area under the curve (AUC) to assess its performance. Finally, we investigated the causal effect of the replicated proteins on MetS using two-sample Mendelian randomization. RESULTS: Prevalent MetS was associated with 116 proteins, of which 53 replicated in HUNT. These included previously reported proteins like leptin, and new proteins like NTR domain-containing protein 2 and endoplasmic reticulum protein 29. Incident MetS was associated with 14 proteins in KORA, of which 13 overlap the prevalent MetS associated proteins with soluble advanced glycosylation end product-specific receptor (sRAGE) being unique to incident MetS. The LASSO selected an eight-protein predictive model with an (AUC = 0.75; 95% CI = 0.71-0.79) in KORA. Mendelian randomization suggested causal effects of three proteins on MetS, namely apolipoprotein E2 (APOE2) (Wald-Ratio = - 0.12, Wald-p = 3.63e-13), apolipoprotein B (APOB) (Wald-Ratio = - 0.09, Wald-p = 2.54e-04) and proto-oncogene tyrosine-protein kinase receptor (RET) (Wald-Ratio = 0.10, Wald-p = 5.40e-04). CONCLUSIONS: Our findings offer new insights into the plasma proteome underlying MetS and identify new protein associations. We reveal possible casual effects of APOE2, APOB and RET on MetS. Our results highlight protein candidates that could potentially serve as targets for prevention and therapy.

Validation of Candidate Phospholipid Biomarkers of Chronic Kidney Disease in Hyperglycemic Individuals and Their Organ-Specific Exploration in Leptin Receptor-Deficient db/db Mouse.

Biological exploration of early biomarkers for chronic kidney disease (CKD) in (pre)diabetic individuals is crucial for personalized management of diabetes. Here, we evaluated two candidate biomarkers of incident CKD (sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0) concerning kidney function in hyperglycemic participants of the Cooperative Health Research in the Region of Augsburg (KORA) cohort, and in two biofluids and six organs of leptin receptor-deficient (db/db) mice and wild type controls. Higher serum concentrations of SM C18:1 and PC aa C38:0 in hyperglycemic individuals were found to be associated with lower estimated glomerular filtration rate (eGFR) and higher odds of CKD. In db/db mice, both metabolites had a significantly lower concentration in urine and adipose tissue, but higher in the lungs. Additionally, db/db mice had significantly higher SM C18:1 levels in plasma and liver, and PC aa C38:0 in adrenal glands. This cross-sectional human study confirms that SM C18:1 and PC aa C38:0 associate with kidney dysfunction in pre(diabetic) individuals, and the animal study suggests a potential implication of liver, lungs, adrenal glands, and visceral fat in their systemic regulation. Our results support further validation of the two phospholipids as early biomarkers of renal disease in patients with (pre)diabetes.

Reversion from prediabetes to normoglycaemia after weight change in older persons: The KORA F4/FF4 study.

BACKGROUND AND AIMS: In a non-interventional study of older persons, we assessed the impact of changes in BMI and waist circumference (WC) on reversion from glucose- and HbA1c-defined prediabetes to normoglycaemia (in short: reversion) and on persistence of normoglycaemia. Moreover, we studied whether reversion reduced cardiovascular risk. METHODS AND RESULTS: From the population-based KORA S4/F4/FF4 cohort study conducted in Southern Germany, we utilized data from the second and third visit to the study center (median follow-up 6.5 years). We used two overlapping data sets, one with 563 persons with HbA1c<6.5% (mean age 69 years, 51.5% men), one with 510 persons with glucose-based prediabetes or normal glucose tolerance. We calculated proportions of reversion, and estimated adjusted relative risks for the association between initial BMI/WC and change of BMI/WC, respectively, and reversion (and persistence of normoglycaemia, respectively). We estimated 10-year cardiovascular risks using the Framingham 2008 score. Overall, 27.3% of persons with HbA1c-defined prediabetes and 9.2% of persons with glucose-based prediabetes returned to normoglycaemia during follow-up. Lower initial BMI/WC and reduction of BMI/WC were associated with larger probabilities of returning to normoglycaemia (e.g., for HbA1c 5.7-6.4%, RR = 1.24 (95% CI: 1.09-1.41) per 1 kg/m2 decline of BMI). Moreover, reduction of BMI/WC increased probabilities of maintaining normoglycaemia (e.g., for glucose-based prediabetes, RR = 1.09 (1.02-1.16) per 1 kg/m2 decline of BMI). 10-year cardiovascular risk was 5.6 (1.7-9.6) percentage points lower after reversion from glucose-based prediabetes to normoglycaemia. CONCLUSION: In older adults, even moderate weight reduction contributes to reversion from prediabetes to normoglycaemia and to maintaining normoglycaemia.

Longitudinal associations between ambient air pollution and insulin sensitivity: results from the KORA cohort study.

BACKGROUND: Impaired insulin sensitivity could be an intermediate step that links exposure to air pollution to the development of type 2 diabetes. However, longitudinal associations of air pollution with insulin sensitivity remain unclear. Our study investigated the associations of long-term air pollution exposure with the degree and rate of change of insulin sensitivity. METHODS: In this longitudinal study, we analysed data from the Cooperative Health Research in the Region of Augsburg (KORA) cohort from Augsburg, Germany, which recruited participants aged 25-74 years in the survey between 1999 and 2001 (KORA S4), with two follow-up examinations in 2006-08 (KORA F4) and 2013-14 (KORA FF4). Serum concentrations of fasting insulin and glucose, and homoeostasis model assessment of insulin resistance (HOMA-IR, a surrogate measure of insulin sensitivity) and ß-cell function (HOMA-B, a surrogate marker for fasting insulin secretion) were assessed at up to three visits between 1999 and 2014. Annual average air pollutant concentrations at the residence were estimated by land-use regression models. We examined the associations of air pollution with repeatedly assessed biomarker levels using mixed-effects models, and we assessed the associations with the annual rate of change in biomarkers using quantile regression models. FINDINGS: Among 9620 observations from 4261 participants in the KORA cohort, we included 6008 (62·5%) observations from 3297 (77·4%) participants in our analyses. Per IQR increment in annual average air pollutant concentrations, HOMA-IR significantly increased by 2·5% (95% CI 0·3 to 4·7) for coarse particulate matter, by 3·1% (0·9 to 5·3) for PM2·5, by 3·6% (1·0 to 6·3) for PM2·5absorbance, and by 3·2% (0·6 to 5·8) for nitrogen dioxide, and borderline significantly increased by 2·2% (-0·1 to 4·5) for ozone, whereas it did not significantly increase for the whole range of ultrafine particles. Similar positive associations in slightly smaller magnitude were observed for HOMA-B and fasting insulin levels. In addition, air pollutant concentrations were positively associated with the annual rate of change in HOMA-IR, HOMA-B, and fasting insulin. Neither the level nor the rate of change of fasting glucose were associated with air pollution exposure. INTERPRETATION: Our study indicates that long-term air pollution exposure could contribute to the development of insulin resistance, which is one of the key factors in the pathogenesis of type 2 diabetes. FUNDING: German Federal Ministry of Education and Research.

ENVINT-D-20-01309: Long-term exposure to air pollution, road traffic noise, residential greenness, and prevalent and incident metabolic syndrome: Results from the population-based KORA F4/FF4 cohort in Augsburg, Germany.

BACKGROUND: A growing number of epidemiological studies show associations between environmental factors and impaired cardiometabolic health. However, evidence is scarce concerning these risk factors and their impact on metabolic syndrome (MetS). This analysis aims to investigate associations between long-term exposure to air pollution, road traffic noise, residential greenness, and MetS. METHODS: We used data of the first (F4, 2006-2008) and second (FF4, 2013-2014) follow-up of the population-based KORA S4 survey in the region of Augsburg, Germany, to investigate associations between exposures and MetS prevalence at F4 (N = 2883) and MetS incidence at FF4 (N = 1192; average follow-up: 6.5 years). Residential long-term exposures to air pollution - including particulate matter (PM) with a diameter < 10 µm (PM10), PM < 2.5 µm (PM2.5), PM between 2.5 and 10 µm (PMcoarse), absorbance of PM2.5 (PM2.5abs), particle number concentration (PNC), nitrogen dioxide (NO2), ozone (O3) - and road traffic noise were modeled by land-use regression models and noise maps. For greenness, the Normalized Difference Vegetation Index (NDVI) was obtained. We estimated Odds Ratios (OR) for single and multi-exposure models using logistic regression and generalized estimating equations adjusted for confounders. Joint Odds Ratios were calculated based on the Cumulative Risk Index. Effect modifiers were examined with interaction terms. RESULTS: We found positive associations between prevalent MetS and interquartile range (IQR) increases in PM10 (OR: 1.15; 95% confidence interval [95% CI]: 1.02, 1.29), PM2.5 (OR: 1.14; 95% CI: 1.02, 1.28), PMcoarse (OR: 1.14; 95% CI: 1.02, 1.27), and PM2.5abs (OR: 1.17; 95% CI: 1.03, 1.32). Results further showed negative, but non-significant associations between exposure to greenness and prevalent and incident MetS. No effects were seen for exposure to road traffic noise. Joint Odds Ratios from multi-exposure models were higher than ORs from models with only one exposure.

A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population.

CONTEXT: Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most. OBJECTIVE: This work aimed to assess whether novel biomarkers improve the prediction of type 2 diabetes beyond noninvasive standard clinical risk factors alone or in combination with glycated hemoglobin A1c (HbA1c). METHODS: We used a population-based case-cohort study for discovery (689 incident cases and 1850 noncases) and an independent cohort study (262 incident cases, 2549 noncases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the noninvasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c). RESULTS: Interleukin-1 receptor antagonist, insulin-like growth factor binding protein 2, soluble E-selectin, decorin, adiponectin, and high-density lipoprotein cholesterol were selected as the most relevant biomarkers. The simultaneous addition of these 6 biomarkers significantly improved the predictive performance both in the discovery (C index [95% CI], 0.053 [0.039-0.066]; cfNRI [95% CI], 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies. CONCLUSION: The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA1c.

Associations between haemoglobin A1c and mortality rate in the KORA S4 and the Heinz Nixdorf Recall population-based cohort studies.

BACKGROUND: There is limited knowledge about mortality risk in persons with increased haemoglobin A1c (HbA1c ) levels below the diabetes threshold. Moreover, little is known about how associations between increased HbA1c and mortality depend on the length of follow-up. Therefore, we studied associations between HbA1c and mortality over long-term follow-up in persons with and without known diabetes. METHODS: We used data from two German population-based cohort studies: KORA S4 Study (Southern Germany, n = 1458, baseline visits in 1999 to 2001, baseline age 55 to 74 years, mortality follow-up 16.8 years) and Heinz Nixdorf Recall (HNR) Study (Ruhr area, n = 4613, baseline visits in 2000 to 2003, baseline age 45 to 75 years, mortality follow-up 17.8 years). Adjusted log-linear models were fitted to estimate relative risks (RRs) with 95% confidence intervals (CI). RESULTS: In both cohorts, participants with HbA1c 39 to 41 mmol/mol (5.7%-5.9%) and HbA1c 42 to 46 mmol/mol (6.0% to 6.4%) did not have a larger overall mortality risk than participants with HbA1c < 39 mmol/mol (5.7%): the corresponding adjusted RRs were 1.00 (95% CI: 0.83-1.21) and 1.01 (0.80-1.27) in KORA and 0.99 (0.82-1.21) and 0.83 (0.65-1.07) in the HNR Study. For the pooled cohorts, the RR for HbA1c 39 to 46 mmol/mol (5.7%-6.4%) was 0.96 (0.85-1.07). Associations between newly detected diabetes (HbA1c ≥ 6.5%) and mortality were weak after 4 and 8 years of follow-up, but were stronger after 12 years of follow-up, whereas associations between previously known diabetes (baseline) and mortality decreased. CONCLUSIONS: HbA1c -defined pre-diabetes is not associated with overall mortality. For newly detected and previously known diabetes, mortality risks vary with length of follow-up.

Multiplatform Approach for Plasma Proteomics: Complementarity of Olink Proximity Extension Assay Technology to Mass Spectrometry-Based Protein Profiling.

The plasma proteome is the ultimate target for biomarker discovery. It stores an endless amount of information on the pathophysiological status of a living organism, which is, however, still difficult to comprehensively access. The high complexity of the plasma proteome can be addressed by either a system-wide and unbiased tool such as mass spectrometry (LC-MS/MS) or a highly sensitive targeted immunoassay such as the proximity extension assay (PEA). To address relevant differences and important shared characteristics, we tested the performance of LC-MS/MS in the data-dependent and data-independent acquisition modes and Olink PEA to measure circulating plasma proteins in 173 human plasma samples from a Southern German population-based cohort. We demonstrated the measurement of more than 300 proteins with both LC-MS/MS approaches applied, mainly including high-abundance plasma proteins. By the use of the PEA technology, we measured 728 plasma proteins, covering a broad dynamic range with high sensitivity down to pg/mL concentrations. Then, we quantified 35 overlapping proteins with all three analytical platforms, verifying the reproducibility of data distributions, measurement correlation, and gender-based differential expression. Our work highlights the limitations and the advantages of both targeted and untargeted approaches and proves their complementary strengths. We demonstrated a significant gain in proteome coverage depth and subsequent biological insight by a combination of platforms-a promising approach for future biomarker and mechanistic studies.

Association of Long-Term Air Pollution with Prevalence and Incidence of Distal Sensorimotor Polyneuropathy: KORA F4/FF4 Study.

BACKGROUND: Air pollution contributes to type 2 diabetes and cardiovascular diseases, but its relevance for other complications of diabetes, in particular distal sensorimotor polyneuropathy (DSPN), is unclear. Recent studies have indicated that DSPN is also increasingly prevalent in obesity. OBJECTIVES: We aimed to assess associations of air pollutants with prevalent and incident DSPN in a population-based study of older individuals with high rates of type 2 diabetes and obesity. METHODS: Cross-sectional analyses on prevalent DSPN were based on 1,075 individuals 62-81 years of age from the German Cooperative Health Research in the Region of Augsburg (KORA) F4 survey (2006-2008). Analyses on incident DSPN included 424 individuals without DSPN at baseline (KORA F4), of whom 188 had developed DSPN by the KORA FF4 survey (2013-2014). Associations of annual average air pollutant concentrations at participants' residences with prevalent and incident DSPN were estimated using Poisson regression models with a robust error variance adjusting for multiple confounders. RESULTS: Higher particle number concentrations (PNCs) were associated with higher prevalence [risk ratio (RR) per interquartile range (IQR) increase=1.10 (95% CI: 1.01, 1.20)] and incidence [1.11 (95% CI: 0.99, 1.24)] of DSPN. In subgroup analyses, particulate (PNC, PM10, PMcoarse, PM2.5, and PM2.5abs) and gaseous (NOx, NO2) pollutants were positively associated with prevalent DSPN in obese participants, whereas corresponding estimates for nonobese participants were close to the null [e.g., for an IQR increase in PNC, RR=1.17 (95% CI: 1.05, 1.31) vs. 1.06 (95% CI: 0.95, 1.19); pinteraction=0.22]. With the exception of PM2.5abs, corresponding associations with incident DSPN were positive in obese participants but null or inverse for nonobese participants, with pinteraction≤0.13 [e.g., for PNC, RR=1.28 (95% CI: 1.08, 1.51) vs. 1.03 (95% CI: 0.90, 1.18); pinteraction=0.03]. DISCUSSION: Both particulate and gaseous air pollutants were positively associated with prevalent and incident DSPN in obese individuals. Obesity and air pollution may have synergistic effects on the development of DSPN. https://doi.org/10.1289/EHP7311.

Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes.

Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin C18:1 and phosphatidylcholine diacyl C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors, and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in people with prediabetes and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.

Deciphering the Plasma Proteome of Type 2 Diabetes.

With an estimated prevalence of 463 million affected, type 2 diabetes represents a major challenge to health care systems worldwide. Analyzing the plasma proteomes of individuals with type 2 diabetes may illuminate hitherto unknown functional mechanisms underlying disease pathology. We assessed the associations between type 2 diabetes and >1,000 plasma proteins in the Cooperative Health Research in the Region of Augsburg (KORA) F4 cohort (n = 993, 110 cases), with subsequent replication in the third wave of the Nord-Trøndelag Health Study (HUNT3) cohort (n = 940, 149 cases). We computed logistic regression models adjusted for age, sex, BMI, smoking status, and hypertension. Additionally, we investigated associations with incident type 2 diabetes and performed two-sample bidirectional Mendelian randomization (MR) analysis to prioritize our results. Association analysis of prevalent type 2 diabetes revealed 24 replicated proteins, of which 8 are novel. Proteins showing association with incident type 2 diabetes were aminoacylase-1, growth hormone receptor, and insulin-like growth factor-binding protein 2. Aminoacylase-1 was associated with both prevalent and incident type 2 diabetes. MR analysis yielded nominally significant causal effects of type 2 diabetes on cathepsin Z and rennin, both known to have roles in the pathophysiological pathways of cardiovascular disease, and of sex hormone-binding globulin on type 2 diabetes. In conclusion, our high-throughput proteomics study replicated previously reported type 2 diabetes-protein associations and identified new candidate proteins possibly involved in the pathogenesis of type 2 diabetes.

Association of endothelial dysfunction with incident prediabetes, type 2 diabetes and related traits: the KORA F4/FF4 study.

INTRODUCTION: Peripheral arterial tonometry (PAT) is an operator-independent and non-invasive measurement method to assess microvascular endothelial function in the fingertips. PAT-derived measures of endothelial function were associated with type 2 diabetes in cross-sectional studies. However, longitudinal studies are lacking. The study aims to investigate the association of two PAT-derived endothelial function parameters reactive hyperemia index (RHI) and mean baseline amplitude (MBA) with follow-up glucose and insulin parameters and the development of (pre)diabetes and type 2 diabetes. RESEARCH DESIGN AND METHODS: The study included 673 participants initially without diabetes (328 men and 345 women) aged 52-71 years from the prospective population-based Cooperative Health Research in the Region of Augsburg F4/FF4 cohort study conducted in Southern Germany (baseline examination F4: 2006-2008; follow-up FF4: 2013-2014). An oral glucose tolerance test was performed at baseline and follow-up to define type 2 diabetes, prediabetes, fasting glucose, fasting insulin, 2-hour glucose, homeostasis model assessment of insulin resistance (HOMA-IR), homeostasis model assessment of beta-cell function and hemoglobin A1c. RESULTS: In multivariable adjusted logistic/linear regression models, a 1 SD increase in baseline RHI was inversely associated with incident type 2 diabetes (OR 0.69 (95% CI 0.48 to 0.97)) as well as with fasting insulin (ß -0.069 (95% CI -0.131 to -0.007)) and HOMA-IR (ß -0.072 (95% CI -0.133 to -0.010)) at follow-up in participants with initial normoglycemia. A 1 SD increase in baseline MBA was positively associated with incident (pre)diabetes (OR 1.62 (95% CI 1.25 to 2.11)) and fasting glucose (ß 0.096 (95% CI 0.047 to 0.146)) at follow-up in participants with initial normoglycemia. CONCLUSIONS: Microvascular endothelial dysfunction seems to be involved in the development of early derangements in glucose metabolism and insulin resistance and could thereby trigger the development of prediabetes and type 2 diabetes.

Association of Dietary Patterns and Type-2 Diabetes Mellitus in Metabolically Homogeneous Subgroups in the KORA FF4 Study.

There is evidence that a change in lifestyle, especially physical activity and diet, can reduce the risk of developing type-2 diabetes mellitus (T2DM). However, the response to dietary changes varies among individuals due to differences in metabolic characteristics. Therefore, we investigated the association between dietary patterns and T2DM while taking into account these differences. For 1287 participants of the population-based KORA FF4 study (Cooperative Health Research in the Region of Augsburg), we identified three metabolically-homogenous subgroups (metabotypes) using 16 clinical markers. Based on usual dietary intake data, two diet quality scores, the Mediterranean Diet Score (MDS) and the Alternate Healthy Eating Index (AHEI), were calculated. We explored the associations between T2DM and diet quality scores. Multi-variable adjusted models, including metabotype subgroup, were fitted. In addition, analyses stratified by metabotype were carried out. We found significant interaction effects between metabotype and both diet quality scores (p < 0.05). In the analysis stratified by metabotype, significant negative associations between T2DM and both diet quality scores were detected only in the metabolically-unfavorable homogenous subgroup (Odds Ratio (OR) = 0.62, 95% confidence interval (CI) = 0.39-0.90 for AHEI and OR = 0.60, 95% CI = 0.40-0.96 for MDS). Prospective studies taking metabotype into account are needed to confirm our results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.

Proinsulin to insulin ratio is associated with incident type 2 diabetes but not with vascular complications in the KORA F4/FF4 study.

INTRODUCTION: We investigated the association of the proinsulin to insulin ratio (PIR) with prevalent and incident type 2 diabetes (T2D), components of the metabolic syndrome, and renal and cardiovascular outcomes in the population-based Cooperative Health Research in the Region of Augsburg (KORA) F4 study (2006-2008)/FF4 study (2013-2014). RESEARCH DESIGN AND METHODS: The analyses included 1514 participants of the KORA F4 study at baseline and 1132 participants of the KORA FF4 study after a median follow-up time of 6.6 years. All-cause and cardiovascular mortality as well as cardiovascular events were analyzed after a median time of 9.1 and 8.6 years, respectively. The association of PIR with T2D, renal and cardiovascular characteristics and mortality were assessed using logistic regression models. Linear regression analyses were used to assess the association of PIR with components of the metabolic syndrome. RESULTS: After adjustment for sex, age, body mass index (BMI), and physical activity, PIR was associated with prevalent (OR: 2.24; 95% CI 1.81 to 2.77; p<0.001) and incident T2D (OR: 1.66; 95% CI 1.26 to 2.17; p<0.001). PIR was associated with fasting glucose (ß per SD: 0.11±0.02; p<0.001) and HbA1c (ß: 0.21±0.02; p<0.001). However, PIR was not positively associated with other components of the metabolic syndrome and was even inversely associated with waist circumference (ß: -0.22±0.03; p<0.001), BMI (ß: -0.11±0.03; p<0.001) and homeostatic model assessment of insulin resistance (ß: -0.22±0.02; p<0.001). PIR was not significantly associated with the intima-media thickness (IMT), decline of kidney function, incident albuminuria, myocardial infarction, stroke, cardiovascular or all-cause mortality. CONCLUSIONS: In the KORA F4/FF4 cohort, PIR was positively associated with prevalent and incident T2D, but inversely associated with waist circumference, BMI and insulin resistance, suggesting that PIR might serve as a biomarker for T2D risk independently of the metabolic syndrome, but not for microvascular or macrovascular complications.

Biomarker-defined pathways for incident type 2 diabetes and coronary heart disease-a comparison in the MONICA/KORA study.

BACKGROUND: Biomarkers may contribute to our understanding of the pathophysiology of various diseases. Type 2 diabetes (T2D) and coronary heart disease (CHD) share many clinical and lifestyle risk factors and several biomarkers are associated with both diseases. The current analysis aims to assess the relevance of biomarkers combined to pathway groups for the development of T2D and CHD in the same cohort. METHODS: Forty-seven serum biomarkers were measured in the MONICA/KORA case-cohort study using clinical chemistry assays and ultrasensitive molecular counting technology. The T2D (CHD) analyses included 689 (568) incident cases and 1850 (2004) non-cases from three population-based surveys. At baseline, the study participants were 35-74 years old. The median follow-up was 14 years. We computed Cox regression models for each biomarker, adjusted for age, sex, and survey. Additionally, we assigned the biomarkers to 19 etiological pathways based on information from literature. One age-, sex-, and survey-controlled average variable was built for each pathway. We used the R2PM coefficient of determination to assess the explained disease risk. RESULTS: The associations of many biomarkers, such as several cytokines or the iron marker soluble transferrin receptor (sTfR), were similar in strength for T2D and CHD, but we also observed important differences. Lipoprotein (a) (Lp(a)) and N-terminal pro B-type natriuretic peptide (NT-proBNP) even demonstrated opposite effect directions. All pathway variables together explained 49% of the T2D risk and 21% of the CHD risk. The insulin-like growth factor binding protein 2 (IGFBP-2, IGF/IGFBP system pathway) best explained the T2D risk (about 9% explained risk, independent of all other pathway variables). For CHD, the myocardial-injury- and lipid-related-pathways were most important and both explained about 4% of the CHD risk. CONCLUSIONS: The biomarker-derived pathway variables explained a higher proportion of the T2D risk compared to CHD. The ranking of the pathways differed between the two diseases, with the IGF/IGFBP-system-pathway being most strongly associated with T2D and the myocardial-injury- and lipid-related-pathways with CHD. Our results help to better understand the pathophysiology of the two diseases, with the ultimate goal of pointing out targets for lifestyle intervention and drug development to ideally prevent both T2D and CHD development.