We used an established mediation analysis to investigate the role of adiposity in the relation between serum 25(OH)D with markers of inflammation and glucose and insulin metabolism. We used data from ...National Health and Nutrition Examination Survey (2005–2010), to evaluate the associations between serum 25(OH)D and markers of insulin resistance (IR) or inflammation, and whether these associations are mediated by adiposity factors. Analysis of co-variance and conceptual causal mediation analysis were conducted taking into consideration the survey design and sample weights. BMI was found to have significant mediation effects (to varied extent) on the associations between serum 25(OH)D and CRP, apo-B, fasting glucose, insulin, HOMA-IR, HOMA-B and HbA1c (all P < 0·05). Both WC and apVAT were also found to partly mediate the associations between serum 25 25(OH)D with CRP, FBG, HbA1c, TAG and HDL-cholesterol (all P < 0·05). These findings support the importance of optimising 25(OH)D status in conditions with abnormal adiposity (i.e. obesity) and treatments for the prevention of cardiometabolic diseases affecting adipose tissue metabolism (i.e. weight loss).
Abstract
Aims
Little is known about the long-term association between low-carbohydrate diets (LCDs) and mortality. We evaluated the link between LCD and overall or cause-specific mortality using both ...individual data and pooled prospective studies.
Methods and results
Data on diets from the National Health and Nutrition Examination Survey (NHANES; 1999–2010) were analysed. Multivariable Cox proportional hazards were applied to determine the hazard ratios and 95% confidence intervals (CIs) for mortality for each quartile of the LCD score, with the lowest quartile (Q1—with the highest carbohydrates intake) used as reference. We used adjusted Cox regression to determine the risk ratio (RR) and 95% CI, as well as random effects models and generic inverse variance methods to synthesize quantitative and pooled data, followed by a leave-one-out method for sensitivity analysis. Overall, 24 825 participants from NHANES study were included (mean follow-up 6.4 years). After adjustment, participants with the lowest carbohydrates intake (quartile 4 of LCD) had the highest risk of overall (32%), cardiovascular disease (CVD) (50%), cerebrovascular (51%), and cancer (36%) mortality. In the same model, the association between LCD and overall mortality was stronger in the non-obese (48%) than in the obese (19%) participants. Findings on pooled data of nine prospective cohort studies with 462 934 participants (mean follow-up 16.1 years) indicated a positive association between LCD and overall (RR 1.22, 95% CI 1.06–1.39, P < 0.001, I2 = 8.6), CVD (RR 1.13, 95% CI 1.02–1.24, P < 0.001, I2 = 11.2), and cancer mortality (RR 1.08, 95% CI 1.01–1.14, P = 0.02, I2 = 10.3). These findings were robust in sensitivity analyses.
Conclusion
Our study suggests a potentially unfavourable association of LCD with overall and cause-specific mortality, based on both new analyses of an established cohort and by pooling previous cohort studies. Given the nature of the study, causality cannot be proven; we cannot rule out residual bias. Nevertheless, further studies are needed to extend these important findings, which if confirmed, may suggest a need to rethink recommendations for LCD in clinical practice.
Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United ...Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866.
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GEOZS, IJS, IMTLJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
The sodium-glucose cotransporter 2 (SGLT2) inhibitors are a class of oral hypoglycemic agents. We undertake a systematic review and meta-analysis of prospective studies to determine the effect of ...SGLT2 on blood pressure (BP) among individuals with type 2 diabetes mellitus.
PubMed-Medline, Web of Science, Cochrane Database, and Google Scholar databases were searched to identify trial registries evaluating the impact of SGLT2 on BP. Random-effects models meta-analysis was used for quantitative data synthesis. The meta-analysis indicated a significant reduction in systolic BP following treatment with SGLT2 (weighted mean difference -2.46 mm Hg 95% CI -2.86 to -2.06). The weighted mean differences for the effect on diastolic BP was -1.46 mm Hg (95% CI -1.82 to -1.09). In these subjects the weighted mean difference effects on serum triglycerides and total cholesterol were -2.08 mg/dL (95% CI -2.51 to -1.64) and 0.77 mg/dL (95% CI 0.33-1.21), respectively. The weighted mean differences for the effect of SGLT2 on body weight was -1.88 kg (95% CI -2.11 to -1.66) across all studies. These findings were robust in sensitivity analyses.
Treatment with SGLT2 glucose cotransporter inhibitors therefore has beneficial off-target effects on BP in patients with type 2 diabetes mellitus and may also be of value in improving other cardiometabolic parameters including lipid profile and body weight in addition to their expected effects on glycemic control. However, our findings should be interpreted with consideration for the moderate statistical heterogeneity across the included studies.
The gut microbiome is shaped by diet and influences host metabolism; however, these links are complex and can be unique to each individual. We performed deep metagenomic sequencing of 1,203 gut ...microbiomes from 1,098 individuals enrolled in the Personalised Responses to Dietary Composition Trial (PREDICT 1) study, whose detailed long-term diet information, as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements were available. We found many significant associations between microbes and specific nutrients, foods, food groups and general dietary indices, which were driven especially by the presence and diversity of healthy and plant-based foods. Microbial biomarkers of obesity were reproducible across external publicly available cohorts and in agreement with circulating blood metabolites that are indicators of cardiovascular disease risk. While some microbes, such as Prevotella copri and Blastocystis spp., were indicators of favorable postprandial glucose metabolism, overall microbiome composition was predictive for a large panel of cardiometabolic blood markers including fasting and postprandial glycemic, lipemic and inflammatory indices. The panel of intestinal species associated with healthy dietary habits overlapped with those associated with favorable cardiometabolic and postprandial markers, indicating that our large-scale resource can potentially stratify the gut microbiome into generalizable health levels in individuals without clinically manifest disease.
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GEOZS, IJS, IMTLJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
We used county level data for T2D prevalence across the mainland USA and matched this to county level ambient PM2.5. Multiple linear regression was used to determine the relation between prevalence ...of T2D with PM2.5 after adjustment for confounding factors. PM2.5 explained 6.3% of the spatial variation in obesity, and 17.9% of the spatial variation in T2D. After correcting the T2D prevalence for obesity, race, poverty, education and temperature, PM2.5 still explained 8.3% of the residual variation in males (P < 0.0001) and 11.5% in females (P < 0.0001). The effect on obesity prevalence corrected for poverty, race education and temperature was much lower and hence the ratio of T2D to obesity prevalence was significantly associated with PM2.5 in males (R
= 11.1%, P < 0.0001) and females (R
= 16.8%, P < 0.0001). This association was repeated across non-African countries (R
= 14.9%, P < 0.0001). High levels of PM2.5 probably contribute to increased T2D prevalence in the USA, but have a more minor effect on the obesity. Exposure to high environmental levels of PM2.5 (relative to the USA) may explain the disproportional risk of T2D in relation to obesity in Asian populations.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Conflicting results suggest a link between serum uric acid (SUA), inflammation and glucose/insulin homeostasis; however, the role of adiposity in this relationship is not clear. Therefore, we ...evaluated the role of different adiposity factors, including central body mass index (BMI), peripheral waist circumference (WC), and visceral adiposity visceral adipose tissue (apVAT), on the association between SUA, inflammation and glucose/insulin homeostasis among US adults.
Data were extracted from the 2005–2010 US National Health and Nutrition Examination Surveys. Overall, 16,502 participants were included in the analysis (mean age = 47.1 years, 48.2% men). Analysis of co-variance and “conceptus causal mediation” models were applied, while accounting for survey design.
Corrected models showed that subjects with higher SUA levels have a less favorable profile of inflammation and glucose/insulin homeostasis parameters (all p < 0.001). We found that all our potential mediators (BMI, WC and apVAT) had an impact (to various extents) on the link between variables, including serum C-reactive protein (CRP), apolipoprotein-B (apoB), insulin resistance markers, 2-h blood glucose (2hG) and triglyceride, and fasting blood glucose (FBG) (TyG) index (all p < .001), while none of the potential mediators (BMI, apVAT, WC) had an impact on the link between FBG and glycated hemoglobin with SUA (all p > 0.05). We have found that all of our mediators partially mediated the link between inflammation and glucose/insulin homeostasis parameters and SUA. Of note, apVAT fully mediated the association between SUA and 2hG.
By applying advanced statistical techniques, we shed light on the complex link of SUA with inflammation and glucose/insulin homeostasis and quantify the role of adiposity factors in that link.
•To evaluated the role of different adiposity factors on the association between serum uric acid, inflammation and glucose/insulin homeostasis.•We accomplished this aim by applying in large and representative sample size of the US adults.•We shed light on the complex link of SUA with inflammation and glucose/insulin homeostasis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Some observational studies indicate a link between blood lead and kidney function although results remain controversial. In this study, Mendelian randomisation (MR) analysis was applied to obtain ...unconfounded estimates of the casual association of genetically determined blood lead with estimated glomerular filtration rate (eGFR) and the risk of chronic kidney disease (CKD). Data from the largest genome-wide association studies (GWAS) on blood lead, eGFR and CKD, from predominantly ethnically European populations, were analysed in total, as well as separately in individuals with or without type 2 diabetes mellitus. Inverse variance weighted (IVW) method, weighted median (WM)-based method, MR-Egger, MR-Pleiotropy RESidual Sum and Outlier (PRESSO) as well as the leave-one-out method were applied. In a general population, lifetime blood lead levels had no significant effect on risk of CKD (IVW:
p
= 0.652) and eGFR (IVW:
p
= 0.668). After grouping by type 2 diabetes status (no diabetes vs. diabetes), genetically higher levels of blood lead had a significant negative impact among subjects with type 2 diabetes (IVW = Beta: −0.03416,
p
= 0.0132) but not in subjects without (IVW:
p
= 0.823), with low likelihood of heterogeneity for any estimates (IVW
p
> 0.158). MR-PRESSO did not highlight any outliers. Pleiotropy test, with very negligible intercept and insignificant
p
-value, indicated a low likelihood of pleiotropy for all estimations. The leave-one-out method demonstrated that links were not driven by a single SNP. Our results show, for the first time, that among subjects with type 2 diabetes, higher blood lead levels are potentially related to less favourable renal function. Further studies are needed to confirm our results.
Key messages
What is already known about this subject?
Chronic kidney disease is associated with unfavourable lifestyle behaviours and conditions such as type 2 diabetes.
Observational studies have reported an association between blood lead and reduced estimated glomerular filtration rate, but the relationship between lead exposure and renal function remains controversial.
What is the key question?
Using Mendelian randomisation with data from 5433 individuals from the UK and Australian populations, does genetically determined blood lead have a potentially causal effect on estimated glomerular filtration rate and the risk of chronic kidney disease?
What are the new findings?
Blood lead levels have a potentially causal effect on reduced renal function in individuals with type 2 diabetes.
In subjects without diabetes, no such causal relationship was identified.
How might this impact on clinical practice in the foreseeable future?
This highlights the risk of elevated blood lead, for example, due to environmental exposure, amongst those with type 2 diabetes, which may predispose them to impaired renal function.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
To investigate the association of triglycerides/glucose index (TyG index), anthropometrically predicted visceral adipose tissue (apVAT), lipid accumulation product (LAP), visceral adiposity index ...(VAI) and triglycerides (TG):high density lipoprotein-cholesterol (HDL-C) ratio with insulin resistance (IR) in adult Americans.
This study was based on data from three NHANES cycles (2005 to 2010). The TyG index was calculated as ln TG×fasting glucose/2. VAI was calculated using gender-specific formulas: men waist circumference (WC)/39.68+(1.88×body mass index (BMI)×(TG/1.03)×(1.31/HDL-C); women: WC/36.58+(1.89×BMI)×(TG/0.81)×(1.52/HDL-C). LAP index was calculated as WC–65×TG in men, and WC–58×TG in women. Correlation and regression analyses accounted for the complex sampling of database.
A total of 18,318 subjects was included in this analysis mean age 47.6Years; 48.7% (n=8918) men. The homeostatic model assessment of insulin resistance (HOMA-IR) had a significant positive correlation with the TyG index (r=0.502), LAP (r=0.551), apVAT (r=0.454), TG:HDL-C ratio (r=0.441) and VAI (r=451) (p<0.001 for all comparisons). Bland-Altman plots showed no systematic errors. The optimal cut-off to predict HOMA-diagnosed IR was 0.473 (sensitivity=74.5% and specificity=72.7%) for LAP, 0.478 (75.9%, 71.9%) for TyG, 0.391 (70.4%, 67.1%) for VAI, 0.392 (77.1% and 62.0%) for TG:HDL-C ratio and 0.381 (63.8%, 74.8%) for apVAT.
The LAP index is a simple, cheap and accurate although not perfect, surrogate marker of HOMA-diagnosed IR among adult Americans. Moreover, it has higher predictability than other screening tools which traditionally applied. Among the markers, apVAT had the highest specificity and the TG:HDL-C ratio had the highest sensitivity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Gut transit time is a key modulator of host-microbiome interactions, yet this is often overlooked, partly because reliable methods are typically expensive or burdensome. The aim of this single-arm, ...single-blinded intervention study is to assess (1) the relationship between gut transit time and the human gut microbiome, and (2) the utility of the 'blue dye' method as an inexpensive and scalable technique to measure transit time.
We assessed interactions between the taxonomic and functional potential profiles of the gut microbiome (profiled via shotgun metagenomic sequencing), gut transit time (measured via the blue dye method), cardiometabolic health and diet in 863 healthy individuals from the PREDICT 1 study.
We found that gut microbiome taxonomic composition can accurately discriminate between gut transit time classes (0.82 area under the receiver operating characteristic curve) and longer gut transit time is linked with specific microbial species such as
,
spp and
spp (false discovery rate-adjusted p values <0.01). The blue dye measure of gut transit time had the strongest association with the gut microbiome over typical transit time proxies such as stool consistency and frequency.
Gut transit time, measured via the blue dye method, is a more informative marker of gut microbiome function than traditional measures of stool consistency and frequency. The blue dye method can be applied in large-scale epidemiological studies to advance diet-microbiome-health research. Clinical trial registry website https://clinicaltrials.gov/ct2/show/NCT03479866 and trial number NCT03479866.
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