Non-Cholesterol Sterol Levels Predict Hyperglycemia and Conversion to Type 2 Diabetes in Finnish Men

Peer reviewe

Genetic basis and outcome in a nationwide study of Finnish patients with hypertrophic cardiomyopathy

Aims Nationwide large-scale genetic and outcome studies in cohorts with hypertrophic cardiomyopathy (HCM) have not been previously published. Methods and results We sequenced 59 cardiomyopathy-associated genes in 382 unrelated Finnish patients with HCM and found 24 pathogenic or likely pathogenic mutations in six genes in 38.2% of patients. Most mutations were located in sarcomere genes (MYBPC3, MYH7, TPM1, and MYL2). Previously reported mutations by our study group (MYBPC3-Gln1061Ter, MYH7-Arg1053Gln, and TPM1-Asp175Asn) and a fourth major mutation MYH7-Val606Met accounted for 28.0% of cases. Mutations in GLA and PRKAG2 were found in three patients. Furthermore, we found 49 variants of unknown significance in 31 genes in 20.4% of cases. During a 6.7 +/- 4.2 year follow-up, annual all-cause mortality in 482 index patients and their relatives with HCM was higher than that in the matched Finnish population (1.70 vs. 0.87%; P <0.001). Sudden cardiac deaths were rare (n = 8). Systolic heart failure (hazard ratio 17.256, 95% confidence interval 3.266-91.170, P = 0.001) and maximal left ventricular wall thickness (hazard ratio 1.223, 95% confidence interval 1.098-1.363, P <0.001) were independent predictors of HCM-related mortality and life-threatening cardiac events. The patients with a pathogenic or likely pathogenic mutation underwent an implantable cardioverter defibrillator implantation more often than patients without a pathogenic or likely pathogenic mutation (12.9 vs. 3.5%, P <0.001), but there was no difference in all-cause or HCM-related mortality between the two groups. Mortality due to HCM during 10 year follow-up among the 5.2 million population of Finland was studied from death certificates of the National Registry, showing 269 HCM-related deaths, of which 32% were sudden. Conclusions We identified pathogenic and likely pathogenic mutations in 38% of Finnish patients with HCM. Four major sarcomere mutations accounted for 28% of HCM cases, whereas HCM-related mutations in non-sarcomeric genes were rare. Mortality in patients with HCM exceeded that of the general population. Finally, among 5.2 million Finns, there were at least 27 HCM-related deaths annually.Peer reviewe

Combined Influence of Waist and Hip Circumference on Risk of Death in a Large Cohort of European and Australian Adults

Background - Waist circumference and hip circumference are both strongly associated with risk of death; however, their joint association has rarely been investigated. Methods and Results - The MONICA Risk, Genetics, Archiving, and Monograph (MORGAM) Project was conducted in 30 cohorts from 11 countries; 90 487 men and women, aged 30 to 74 years, predominantly white, with no history of cardiovascular disease, were recruited in 1986 to 2010 and followed up for up to 24 years. Hazard ratios were estimated using sex‐specific Cox models, stratified by cohort, with age as the time scale. Models included baseline categorical obesity measures, age, total and high‐density lipoprotein cholesterol, systolic blood pressure, antihypertensive drugs, smoking, and diabetes mellitus. A total of 9105 all‐cause deaths were recorded during a median follow‐up of 10 years. Hazard ratios for all‐cause death presented J‐ or U‐shaped associations with most obesity measures. With waist and hip circumference included in the same model, for all hip sizes, having a smaller waist was strongly associated with lower risk of death, except for men with the smallest hips. In addition, among those with smaller waists, hip size was strongly negatively associated with risk of death, with ≈20% more people identified as being at increased risk compared with waist circumference alone. Conclusions - A more complex relationship between hip circumference, waist circumference, and risk of death is revealed when both measures are considered simultaneously. This is particularly true for individuals with smaller waists, where having larger hips was protective. Considering both waist and hip circumference in the clinical setting could help to best identify those at increased risk of death

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Partial correlations between sterol levels and fasting plasma glucose (FPG), and 2-hour plasma glucose (2hPG), adjusted for age, BMI, smoking, physical activity, and additionally for insulin sensitivity and insulin secretion.

<p>Individuals with previously diagnosed diabetes and on statin or ezetimibe treatment were excluded (N = 727–740). Bold types indicate statistical significance with <i>P</i>≤0.006.</p

Non-cholesterol sterols indicating cholesterol synthesis (diamonds) and absorption (circles) in relation to hyperglycemia, according to the categories of fasting (FPG) and 2-hour plasma glucose (2hPG) levels (N = 746).

<p>Data points represent unadjusted means of sterol levels in each glucose category. <i>P</i> values were calculated using general linear model adjusted for age, BMI, smoking, and physical activity: **<i>P</i><0.01, ***<i>P</i><10⁻⁵. Individuals with previously diagnosed diabetes and on statin or ezetimibe treatment were excluded.</p

Association between baseline sterol levels and newly developed type 2 diabetes in a 5-year follow-up study.

<p>Cox regression analysis. Participants with newly diagnosed type 2 diabetes at follow-up (N = 33) were compared to those with NGT/NFG and HbA1c <6.5% at baseline and follow-up examinations (N = 97). Diabetes diagnosis was defined as FPG ≥7.0mmol/l, 2hPG ≥11.1 mmol/l and/or HbA1c ≥6.5%. Individuals with previously diagnosed diabetes or with statin or ezetimibe treatment were excluded. Sterol levels are adjusted for total cholesterol and divided by 10. Bold types indicate statistical significance with <i>P</i>≤0.006.</p><p>P, unadjusted.</p><p>P* adjusted for age, BMI, smoking, and physical activity.</p><p>P^† adjusted for age, BMI, smoking, and physical activity and Matsuda ISI.</p><p>P^‡ adjusted for age, BMI, smoking, and physical activity and Matsuda ISI, InsulinAUC_0–30/GlucoseAUC_0–30, glucose AUC at baseline, and total triglycerides.</p

Genotyping results: 1^st stage results obtained from genome-wide association study (EUGENE2 Study), 2^nd stage results obtained from replication study (METSIM Study), and meta-analysis of both cohorts.

<p>Only results from meta-analysis indicating a genome-wide significance (P<5×10⁻⁸) are shown. β = Regression coefficient. Individuals with previously diagnosed diabetes and on statin or ezetimibe treatment are excluded. P values adjusted for age, gender, and BMI.</p