Global Effect of Modifiable Risk Factors on Cardiovascular Disease and Mortality.

BACKGROUND: Five modifiable risk factors are associated with cardiovascular disease and death from any cause. Studies using individual-level data to evaluate the regional and sex-specific prevalence of the risk factors and their effect on these outcomes are lacking. METHODS: We pooled and harmonized individual-level data from 112 cohort studies conducted in 34 countries and 8 geographic regions participating in the Global Cardiovascular Risk Consortium. We examined associations between the risk factors (body-mass index, systolic blood pressure, non-high-density lipoprotein cholesterol, current smoking, and diabetes) and incident cardiovascular disease and death from any cause using Cox regression analyses, stratified according to geographic region, age, and sex. Population-attributable fractions were estimated for the 10-year incidence of cardiovascular disease and 10-year all-cause mortality. RESULTS: Among 1,518,028 participants (54.1% of whom were women) with a median age of 54.4 years, regional variations in the prevalence of the five modifiable risk factors were noted. Incident cardiovascular disease occurred in 80,596 participants during a median follow-up of 7.3 years (maximum, 47.3), and 177,369 participants died during a median follow-up of 8.7 years (maximum, 47.6). For all five risk factors combined, the aggregate global population-attributable fraction of the 10-year incidence of cardiovascular disease was 57.2% (95% confidence interval [CI], 52.4 to 62.1) among women and 52.6% (95% CI, 49.0 to 56.1) among men, and the corresponding values for 10-year all-cause mortality were 22.2% (95% CI, 16.8 to 27.5) and 19.1% (95% CI, 14.6 to 23.6). CONCLUSIONS: Harmonized individual-level data from a global cohort showed that 57.2% and 52.6% of cases of incident cardiovascular disease among women and men, respectively, and 22.2% and 19.1% of deaths from any cause among women and men, respectively, may be attributable to five modifiable risk factors. (Funded by the German Center for Cardiovascular Research (DZHK); ClinicalTrials.gov number, NCT05466825.).

EAT-Lancet Healthy Reference Diet score and diabetes incidence in a cohort of Mexican women.

To improve the health of our planet and develop sustainable food policies, it is important to understand the health impact of a diet pattern that considers planetary and population health. We used data from the Mexican Teachers' Cohort (MTC) to estimate the association between the EAT-Lancet healthy reference diet (EAT-HRD) and type 2 diabetes (T2D) incidence. We included 74,671 women aged ≥25 years, free of T2D at baseline. A validated food frequency questionnaire (FFQ) was used to assess dietary intake. We created an EAT-HRD score based on 15 food groups recommended by the EAT-Lancet Commission (range from 0 to 15). T2D cases were identified through self-report and cross-linkage with clinical and administrative databases. We used Cox proportional hazards models to estimate the association between categories of the EAT-HRD score with T2D incidence. During a median follow-up of 2.16 y (IQR 1.8-4.3 y), we identified 3241 T2D incident cases. The median EAT-HRD score was 6 (IQR 5-7). In multivariable analyses, when comparing extreme categories, higher adherence to the EAT-HRD score was associated with lower T2D incidence (HR 0.90; 95% CI 0.75, 1.10), yet, the estimation was imprecise. Compared to those who did not meet the EAT-HRD recommendations, adhering to the red meat, legumes, and fish recommendations was associated with lower T2D incidence. Meeting the recommendation of dairy and added sugars was associated with an increased incidence of T2D. Higher adherence to a diet designed to promote environmental and human health may help prevent T2D incidence among women in a middle-income country.

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome.

BACKGROUND: Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals. METHODS: We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS. RESULTS: We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms. CONCLUSIONS: These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain.

BACKGROUND: Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches. METHODS AND RESULTS: Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from the Mexico City Diabetes Study (MCDS; n = 768), in which one standard deviation increase in the MRS corresponded to ~0.03 increase in BMI (kg/m2) per year (i.e. ~0.09 kg/year for a 1.7 m adult). We observed that none of the available anthropometric, lifestyle, and glycemic variables fully account for the MRS prediction of weight gain. Surprisingly, we found the MRS to be strongly correlated with baseline insulin sensitivity in both cohorts and to be negatively predictive of T2D in MCDS. Genome-wide association study of the MRS identified 2 genome-wide (p < 5 × 10-8) and 5 suggestively (p < 1 × 10-6) significant loci, several of which have been previously linked to obesity-related phenotypes. CONCLUSIONS: We have constructed and validated a generalizable MRS for future weight gain that is an independent predictor distinct from several other known risk factors. The MRS captures a composite biological picture of weight gain, perhaps hinting at the anabolic effects of preserved insulin sensitivity. Future investigation is required to assess the relationships between MRS-predicted weight gain and other obesity-related diseases.

Polyunsaturated Fatty Acid Desaturation Is a Mechanism for Glycolytic NAD+ Recycling.

The reactions catalyzed by the delta-5 and delta-6 desaturases (D5D/D6D), key enzymes responsible for highly unsaturated fatty acid (HUFA) synthesis, regenerate NAD+ from NADH. Here, we show that D5D/D6D provide a mechanism for glycolytic NAD+ recycling that permits ongoing glycolysis and cell viability when the cytosolic NAD+/NADH ratio is reduced, analogous to lactate fermentation. Although lesser in magnitude than lactate production, this desaturase-mediated NAD+ recycling is acutely adaptive when aerobic respiration is impaired in vivo. Notably, inhibition of either HUFA synthesis or lactate fermentation increases the other, underscoring their interdependence. Consistent with this, a type 2 diabetes risk haplotype in SLC16A11 that reduces pyruvate transport (thus limiting lactate production) increases D5D/D6D activity in vitro and in humans, demonstrating a chronic effect of desaturase-mediated NAD+ recycling. These findings highlight key biologic roles for D5D/D6D activity independent of their HUFA end products and expand the current paradigm of glycolytic NAD+ regeneration.

PAIRUP-MS: Pathway analysis and imputation to relate unknowns in profiles from mass spectrometry-based metabolite data.

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70-88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.

Comparing different definitions of prediabetes with subsequent risk of diabetes: an individual participant data meta-analysis involving 76 513 individuals and 8208 cases of incident diabetes.

Objective: There are currently five widely used definition of prediabetes. We compared the ability of these to predict 5-year conversion to diabetes and investigated whether there were other cut-points identifying risk of progression to diabetes that may be more useful. Research design and methods: We conducted an individual participant meta-analysis using longitudinal data included in the Obesity, Diabetes and Cardiovascular Disease Collaboration. Cox regression models were used to obtain study-specific HRs for incident diabetes associated with each prediabetes definition. Harrell's C-statistics were used to estimate how well each prediabetes definition discriminated 5-year risk of diabetes. Spline and receiver operating characteristic curve (ROC) analyses were used to identify alternative cut-points. Results: Sixteen studies, with 76 513 participants and 8208 incident diabetes cases, were available. Compared with normoglycemia, current prediabetes definitions were associated with four to eight times higher diabetes risk (HRs (95% CIs): 3.78 (3.11 to 4.60) to 8.36 (4.88 to 14.33)) and all definitions discriminated 5-year diabetes risk with good accuracy (C-statistics 0.79-0.81). Cut-points identified through spline analysis were fasting plasma glucose (FPG) 5.1 mmol/L and glycated hemoglobin (HbA1c) 5.0% (31 mmol/mol) and cut-points identified through ROC analysis were FPG 5.6 mmol/L, 2-hour postload glucose 7.0 mmol/L and HbA1c 5.6% (38 mmol/mol). Conclusions: In terms of identifying individuals at greatest risk of developing diabetes within 5 years, using prediabetes definitions that have lower values produced non-significant gain. Therefore, deciding which definition to use will ultimately depend on the goal for identifying individuals at risk of diabetes.

Comparison of relationships between four common anthropometric measures and incident diabetes.

AIMS: First, to conduct a detailed exploration of the prospective relations between four commonly used anthropometric measures with incident diabetes and to examine their consistency across different population subgroups. Second, to compare the ability of each of the measures to predict five-year risk of diabetes. METHODS: We conducted a meta-analysis of individual participant data on body mass index (BMI), waist circumference (WC), waist-hip and waist-height ratio (WHtR) from the Obesity, Diabetes and Cardiovascular Disease Collaboration. Cox proportional hazard models were used to estimate the association between a one standard deviation increment in each anthropometric measure and incident diabetes. Harrell's concordance statistic was used to test the predictive accuracy of each measure for diabetes risk at five years. RESULTS: Twenty-one studies with 154,998 participants and 9342 cases of incident diabetes were available. Each of the measures had a positive association with incident diabetes. A one standard deviation increment in each of the measures was associated with 64-80% higher diabetes risk. WC and WHtR more strongly associated with risk than BMI (ratio of hazard ratios: 0.95 [0.92,0.99] - 0.97 [0.95,0.98]) but there was no appreciable difference between the four measures in the predictive accuracy for diabetes at five years. CONCLUSIONS: Despite suggestions that abdominal measures of obesity have stronger associations with incident diabetes and better predictive accuracy than BMI, we found no overall advantage in any one measure at discriminating the risk of developing diabetes. Any of these measures would suffice to assist in primary diabetes prevention efforts.

Home C-ABPM for Preventive and Curative Health Care and Transdisciplinary Science.

The clinical everyday management of blood pressure (BP) and heart rate (HR) can be greatly improved by the mapping of time structures in home ambulatory BP and HR assessment. Thereby, we change focus from the BP and the HR to the dynamics of these variables. This change is achieved by computer-implemented chronomics, the mapping of chronomes, consisting of cyclicities (our concern herein) along with chaos and trends, in the service of cardiologists, general health care providers, the educated public, and transdisciplinary science. We here further illustrate the yield of chronomics in research on long BP and HR series covering years, some several decades long, and on archives of human sudden cardiac death revealing magnetoperiodisms, e.g., "years" longer than a calendar year, i.e., transyears. In this case of cardiac arrest, what we do not see, the 16- to 20-month transyear is prominent, in the absence of any signature of the calendar year, and so can be a cis-half-year of about 5 months.

Prediction of type 2 diabetes using simple measures of insulin resistance: combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study.

To determine and formally compare the ability of simple indexes of insulin resistance (IR) to predict type 2 diabetes, we used combined prospective data from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study, which include well-characterized cohorts of non-Hispanic white, African-American, Hispanic American, and Mexican subjects with 5-8 years of follow-up. Poisson regression was used to assess the ability of each candidate index to predict incident diabetes at the follow-up examination (343 of 3,574 subjects developed diabetes). The areas under the receiver operator characteristic (AROC) curves for each index were calculated and statistically compared. In pooled analysis, Gutt et al.'s insulin sensitivity index at 0 and 120 min (ISI(0,120)) displayed the largest AROC (78.5%). This index was significantly more predictive (P < 0.0001) than a large group of indexes (including those by Belfiore, Avignon, Katz, and Stumvoll) that had AROC curves between 66 and 74%. These findings were essentially similar both after adjustment for covariates and when analyses were conducted separately by glucose tolerance status and ethnicity/study subgroups. In conclusion, we found substantial differences between published IR indexes in the prediction of diabetes, with ISI(0,120) consistently showing the strongest prediction. This index may reflect other aspects of diabetes pathogenesis in addition to IR, which might explain its strong predictive abilities despite its moderate correlation with direct measures of IR.

Alteraciones cardiovasculares en la agromegalia: estudio prospectivo / Cardiovascular complications in acromegaly: prospective study

Se presentan 38 casos con acromegalia estudiados desde el punto de vista cardiológico a través de métodos no invasivos para detectar la frecuencia de complicaciones cardiovasculares. El 71% de los casos presentaron algun tipo de alteración cardiovascular. En el 68% observamos crecimiento ventricular izquierdo por ecocardiografía, el cuál resulto ser el método más sensible para detectar este cambio. El 71% de los electrocardiogramas fueron anormales, siendo los transtornos de conducción los más frecuentes, principalmente, el bloqueio de rama derecha. En la mitad de los casos se observó fibrosis pulmonar y bronquitis crónica. Hipertensión arterial estuvo presente en el 32% y diabetes mellitus en el 21%. Sólo 2 casos presentaron datos de cardiopatía coronaria. Al 37% se les ha practicado hipofisectomia con regresión en un 90% de las alteraciones encontradas, excepto por el crecimiento ventricular izquierdo y la fibrosis pulmonar. No se observó ninguna defunción