BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization

Risk factors for type 2 diabetes mellitus: An exposure-wide umbrella review of meta-analyses

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a global epidemic associated with increased health expenditure, and low quality of life. Many non-genetic risk factors have been suggested, but their overall epidemiological credibility has not been assessed.

METHODS

We searched PubMed to capture all meta-analyses and Mendelian randomization studies for risk factors of T2DM. For each association, we estimated the summary effect size, its 95% confidence and prediction interval, and the I2 metric. We examined the presence of small-study effects and excess significance bias. We assessed the epidemiological credibility through a set of predefined criteria.

RESULTS

We captured 86 eligible papers (142 associations) covering a wide range of biomarkers, medical conditions, and dietary, lifestyle, environmental and psychosocial factors. Adiposity, low hip circumference, serum biomarkers (increased level of alanine aminotransferase, gamma-glutamyl transferase, uric acid and C-reactive protein, and decreased level of adiponectin and vitamin D), an unhealthy dietary pattern (increased consumption of processed meat and sugar-sweetened beverages, decreased intake of whole grains, coffee and heme iron, and low adherence to a healthy dietary pattern), low level of education and conscientiousness, decreased physical activity, high sedentary time and duration of television watching, low alcohol drinking, smoking, air pollution, and some medical conditions (high systolic blood pressure, late menarche age, gestational diabetes, metabolic syndrome, preterm birth) presented robust evidence for increased risk of T2DM.

CONCLUSIONS

A healthy lifestyle pattern could lead to decreased risk for T2DM. Future randomized clinical trials should focus on identifying efficient strategies to modify harmful daily habits and predisposing dietary patterns.

Interrelationship of the rs7903146 TCF7L2 gene variant with measures of glucose metabolism and adiposity: The NEO study

BACKGROUND AND AIMS

We investigated the interrelationship of rs7903146-T in TCF7L2 with measures of glucose metabolism and measures of adiposity.

METHODS AND RESULTS

This cross-sectional analysis was conducted in 5744 middle-aged participants (mean (standard deviation [SD]) age is 55.9 (6.0) years) from the Netherlands Epidemiology of Obesity (NEO) Study. Associations between rs7903146-T and Type 2 diabetes mellitus (T2D) were assessed with logistic regression. Additive (per-allele) associations with measures of glucose metabolism (e.g., fasting insulin) and adiposity (e.g., body mass index [BMI]) were examined with multivariable linear regression. In the total study population, rs7903146-T was associated with a higher risk of T2D (additive odds ratio: 1.42; 95% confidence interval: 1.17; 1.72), and specifically with T2D treated with insulin analogs (2.31 [1.19; 4.46]). After exclusion of participants treated with glucose-lowering medication, rs7903146-T was associated with lower mean insulin concentration (additive mean difference: -0.07 SD [-0.14; 0.00]), but not with higher mean glucose concentration (0.03 SD [-0.01; 0.07]). Furthermore, rs7903146-T was associated with, among other measures of adiposity, a lower mean BMI (-0.04 SD [-0.09; -0.00]), and a lower mean total body fat (-0.04 SD [-0.08; -0.00]). The association between rs7903146-T and T2D increased after adjustment for BMI (odds ratio: 1.51 [1.24; 1.86]); the association between rs7903146-T and fasting insulin diminished after adjustment (-0.05 SD [-0.11; 0.02]).

CONCLUSION

rs7903146-T is associated with a decreased insulin concentration and increased risk of T2D with opposing effects of adjustment for adiposity.

Reassessing Ethnic Differences in Mean BMI and Changes Between 2007 and 2013 in English Children

OBJECTIVE

National body fatness (BF) data for English South Asian and Black children use BMI, which provides inaccurate ethnic comparisons. BF levels and time trends in the English National Child Measurement Programme (NCMP) between 2007 and 2013 were assessed by using ethnic-specific adjusted BMI (aBMI) for South Asian and Black children.

METHODS

Analyses were based on 3,195,323 children aged 4 to 5 years and 2,962,673 children aged 10 to 11 years. aBMI values for South Asian and Black children (relating to BF as in White children) were derived independently. Mean aBMI levels and 5-year aBMI changes were obtained by using linear regression.

RESULTS

In the 2007-2008 NCMP, mean aBMIs in 10- to 11-year-old children (boys, girls) were higher in South Asian children (20.1, 19.9 kg/m² ) and Black girls, but not in Black boys (18.4, 19.2 kg/m² ) when compared with White children (18.6, 19.0 kg/m² ; all P < 0.001). Mean 5-year changes (boys, girls) were higher in South Asian children (0.16, 0.32 kg/m² per 5 y; both P < 0.001) and Black boys but not girls (0.13, 0.15 kg/m² per 5 y; P = 0.01, P = 0.41) compared with White children (0.02, 0.11 kg/m² per 5 y). Ethnic differences at 4 to 5 years were similar. Unadjusted BMI showed similar 5-year changes but different mean BMI patterns.

CONCLUSIONS

BF levels were higher in South Asian children than in other groups in 2007 and diverged from those in White children until 2013, a pattern not apparent from unadjusted BMI data.

Inflammatory Biomarkers and Risk of Schizophrenia: A 2-Sample Mendelian Randomization Study

IMPORTANCE

Positive associations between inflammatory biomarkers and risk of psychiatric disorders, including schizophrenia, have been reported in observational studies. However, conventional observational studies are prone to bias, such as reverse causation and residual confounding, thus limiting our understanding of the effect (if any) of inflammatory biomarkers on schizophrenia risk.

OBJECTIVE

To evaluate whether inflammatory biomarkers have an effect on the risk of developing schizophrenia.

DESIGN, SETTING, AND PARTICIPANTS

Two-sample mendelian randomization study using genetic variants associated with inflammatory biomarkers as instrumental variables to improve inference. Summary association results from large consortia of candidate gene or genome-wide association studies, including several epidemiologic studies with different designs, were used. Gene-inflammatory biomarker associations were estimated in pooled samples ranging from 1645 to more than 80 000 individuals, while gene-schizophrenia associations were estimated in more than 30 000 cases and more than 45 000 ancestry-matched controls. In most studies included in the consortia, participants were of European ancestry, and the prevalence of men was approximately 50%. All studies were conducted in adults, with a wide age range (18 to 80 years).

EXPOSURES

Genetically elevated circulating levels of C-reactive protein (CRP), interleukin-1 receptor antagonist (IL-1Ra), and soluble interleukin-6 receptor (sIL-6R).

MAIN OUTCOMES AND MEASURES

Risk of developing schizophrenia. Individuals with schizophrenia or schizoaffective disorders were included as cases. Given that many studies contributed to the analyses, different diagnostic procedures were used.

RESULTS

The pooled odds ratio estimate using 18 CRP genetic instruments was 0.90 (random effects 95% CI, 0.84-0.97; P = .005) per 2-fold increment in CRP levels; consistent results were obtained using different mendelian randomization methods and a more conservative set of instruments. The odds ratio for sIL-6R was 1.06 (95% CI, 1.01-1.12; P = .02) per 2-fold increment. Estimates for IL-1Ra were inconsistent among instruments, and pooled estimates were imprecise and centered on the null.

CONCLUSIONS AND RELEVANCE

Under mendelian randomization assumptions, our findings suggest a protective effect of CRP and a risk-increasing effect of sIL-6R (potentially mediated at least in part by CRP) on schizophrenia risk. It is possible that such effects are a result of increased susceptibility to early life infection.

Novel Common Variants Associated with Obesity and Type 2 Diabetes Detected Using a cFDR Method

Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and obesity are generally focused on individual traits independently, and genetic intercommunity (common genetic contributions or the product of over correlated phenotypic world) between them are largely unknown, despite extensive data showing that these two phenotypes share both genetic and environmental risk factors. Here, we applied a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with BMI and T2D by incorporating the summary statistics from existing GWASs of these two traits. Conditional Q-Q and fold enrichment plots were used to visually demonstrate the strength of pleiotropic enrichment. Adopting a cFDR nominal significance level of 0.05, 287 loci were identified for BMI and 75 loci for T2D, 23 of which for both traits. By incorporating related traits into a conditional analysis framework, we observed significant pleiotropic enrichment between obesity and T2D. These findings may provide novel insights into the etiology of obesity and T2D, individually and jointly.

Recent Developments in Mendelian Randomization Studies

PURPOSE OF REVIEW

Mendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel's First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions.

RECENT FINDINGS

In this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR.

SUMMARY

In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.

Patterns of childhood body mass index (BMI), overweight and obesity in South Asian and black participants in the English National child measurement programme: effect of applying BMI adjustments standardising for ethnic differences in BMI-body fatness associations

Background:

The National Child Measurement Programme (NCMP) records weight and height and assesses overweight-obesity patterns in English children using body mass index (BMI), which tends to underestimate body fatness in South Asian children and overestimate body fatness in Black children of presumed African ethnicity. Using BMI adjustments to ensure that adjusted BMI was similarly related to body fatness in South Asian, Black and White children, we reassessed population overweight and obesity patterns in these ethnic groups in NCMP.

Methods:

Analyses were based on 2012–2013 NCMP data in 582 899 children aged 4–5 years and 485 362 children aged 10–11 years. Standard centile-based approaches defined weight status in each age group before and after applying BMI adjustments for English South Asian and Black children derived from previous studies using the deuterium dilution method.

Findings:

Among White children, overweight-obesity prevalences (boys, girls) were 23% and 21%, respectively, in 4–5 year olds and 33% and 30%, respectively, in 10–11 year olds. Before adjustment, South Asian children had lower overweight-obesity prevalences at 4–5 years (19%, 19%) and slightly higher prevalences at 10–11 years (42%, 34%), whereas Black children had higher overweight-obesity prevalences both at 4–5 years (31%, 29%) and 10–11 years (42%, 45%). Following adjustment, overweight-obesity prevalences were markedly higher in South Asian children both at 4–5 years (39%, 35%) and at 10–11 years (52%, 44%), whereas Black children had lower prevalences at 4–5 years (11%, 12%); at 10–11 years, prevalences were slightly lower in boys (32%) but higher in girls (35%).

Interpretation:

BMI adjustments revealed extremely high overweight-obesity prevalences among South Asian children in England, which were not apparent in unadjusted data. In contrast, after adjustment, Black children had lower overweight-obesity prevalences except among older girls.

Funding:

British Heart Foundation, NIHR CLAHRC (South London), NIHR CLAHRC (North Thames).

Bone mineral density and risk of type 2 diabetes and coronary heart disease: A Mendelian randomization study

Background: Observational studies have demonstrated that increased bone mineral density is associated with a higher risk of type 2 diabetes (T2D), but the relationship with risk of coronary heart disease (CHD) is less clear. Moreover, substantial uncertainty remains about the causal relevance of increased bone mineral density for T2D and CHD, which can be assessed by Mendelian randomisation studies.  Methods: We identified 235 independent single nucleotide polymorphisms (SNPs) associated at p<5×10 ^-8 with estimated heel bone mineral density (eBMD) in 116,501 individuals from the UK Biobank study, accounting for 13.9% of eBMD variance. For each eBMD-associated SNP, we extracted effect estimates from the largest available GWAS studies for T2D (DIAGRAM: n=26,676 T2D cases and 132,532 controls) and CHD (CARDIoGRAMplusC4D: n=60,801 CHD cases and 123,504 controls). A two-sample design using several Mendelian randomization approaches was used to investigate the causal relevance of eBMD for risk of T2D and CHD. In addition, we explored the relationship of eBMD, instrumented by the 235 SNPs, on 12 cardiovascular and metabolic risk factors. Finally, we conducted Mendelian randomization analysis in the reverse direction to investigate reverse causality. Results: Each one standard deviation increase in genetically instrumented eBMD (equivalent to 0.14 g/cm ²) was associated with an 8% higher risk of T2D (odds ratio [OR] 1.08; 95% confidence interval [CI]: 1.02 to 1.14; p=0.012) and 5% higher risk of CHD (OR 1.05; 95%CI: 1.00 to 1.10; p=0.034). Consistent results were obtained in sensitivity analyses using several different Mendelian randomization approaches. Equivalent increases in eBMD were also associated with lower plasma levels of HDL-cholesterol and increased insulin resistance. Mendelian randomization in the reverse direction using 94 T2D SNPs or 52 CHD SNPs showed no evidence of reverse causality with eBMD. Conclusions: These findings suggest a causal relationship between elevated bone mineral density with risks of both T2D and CHD.

Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe

BACKGROUND

Although overweight and obesity have been studied in relation to individual cardiometabolic diseases, their association with risk of cardiometabolic multimorbidity is poorly understood. Here we aimed to establish the risk of incident cardiometabolic multimorbidity (ie, at least two from: type 2 diabetes, coronary heart disease, and stroke) in adults who are overweight and obese compared with those who are a healthy weight.

METHODS

We pooled individual-participant data for BMI and incident cardiometabolic multimorbidity from 16 prospective cohort studies from the USA and Europe. Participants included in the analyses were 35 years or older and had data available for BMI at baseline and for type 2 diabetes, coronary heart disease, and stroke at baseline and follow-up. We excluded participants with a diagnosis of diabetes, coronary heart disease, or stroke at or before study baseline. According to WHO recommendations, we classified BMI into categories of healthy (20·0-24·9 kg/m2), overweight (25·0-29·9 kg/m2), class I (mild) obesity (30·0-34·9 kg/m2), and class II and III (severe) obesity (≥35·0 kg/m2). We used an inclusive definition of underweight (<20 kg/m2) to achieve sufficient case numbers for analysis. The main outcome was cardiometabolic multimorbidity (ie, developing at least two from: type 2 diabetes, coronary heart disease, and stroke). Incident cardiometabolic multimorbidity was ascertained via resurvey or linkage to electronic medical records (including hospital admissions and death). We analysed data from each cohort separately using logistic regression and then pooled cohort-specific estimates using random-effects meta-analysis.

FINDINGS

Participants were 120  813 adults (mean age 51·4 years, range 35-103; 71 445 women) who did not have diabetes, coronary heart disease, or stroke at study baseline (1973-2012). During a mean follow-up of 10·7 years (1995-2014), we identified 1627 cases of multimorbidity. After adjustment for sociodemographic and lifestyle factors, compared with individuals with a healthy weight, the risk of developing cardiometabolic multimorbidity in overweight individuals was twice as high (odds ratio [OR] 2·0, 95% CI 1·7-2·4; p<0·0001), almost five times higher for individuals with class I obesity (4·5, 3·5-5·8; p<0·0001), and almost 15 times higher for individuals with classes II and III obesity combined (14·5, 10·1-21·0; p<0·0001). This association was noted in men and women, young and old, and white and non-white participants, and was not dependent on the method of exposure assessment or outcome ascertainment. In analyses of different combinations of cardiometabolic conditions, odds ratios associated with classes II and III obesity were 2·2 (95% CI 1·9-2·6) for vascular disease only (coronary heart disease or stroke), 12·0 (8·1-17·9) for vascular disease followed by diabetes, 18·6 (16·6-20·9) for diabetes only, and 29·8 (21·7-40·8) for diabetes followed by vascular disease.

INTERPRETATION

The risk of cardiometabolic multimorbidity increases as BMI increases; from double in overweight people to more than ten times in severely obese people compared with individuals with a healthy BMI. Our findings highlight the need for clinicians to actively screen for diabetes in overweight and obese patients with vascular disease, and pay increased attention to prevention of vascular disease in obese individuals with diabetes.

FUNDING

NordForsk, Medical Research Council, Cancer Research UK, Finnish Work Environment Fund, and Academy of Finland.

Interpreting findings from Mendelian randomization using the MR-Egger method

Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption-the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases.

Assessing causality in associations between cannabis use and schizophrenia risk: a two-sample Mendelian randomization study

BACKGROUND

Observational associations between cannabis and schizophrenia are well documented, but ascertaining causation is more challenging. We used Mendelian randomization (MR), utilizing publicly available data as a method for ascertaining causation from observational data.

METHOD

We performed bi-directional two-sample MR using summary-level genome-wide data from the International Cannabis Consortium (ICC) and the Psychiatric Genomics Consortium (PGC2). Single nucleotide polymorphisms (SNPs) associated with cannabis initiation (p < 10-5) and schizophrenia (p < 5 × 10-8) were combined using an inverse-variance-weighted fixed-effects approach. We also used height and education genome-wide association study data, representing negative and positive control analyses.

RESULTS

There was some evidence consistent with a causal effect of cannabis initiation on risk of schizophrenia [odds ratio (OR) 1.04 per doubling odds of cannabis initiation, 95% confidence interval (CI) 1.01-1.07, p = 0.019]. There was strong evidence consistent with a causal effect of schizophrenia risk on likelihood of cannabis initiation (OR 1.10 per doubling of the odds of schizophrenia, 95% CI 1.05-1.14, p = 2.64 × 10-5). Findings were as predicted for the negative control (height: OR 1.00, 95% CI 0.99-1.01, p = 0.90) but weaker than predicted for the positive control (years in education: OR 0.99, 95% CI 0.97-1.00, p = 0.066) analyses.

CONCLUSIONS

Our results provide some that cannabis initiation increases the risk of schizophrenia, although the size of the causal estimate is small. We find stronger evidence that schizophrenia risk predicts cannabis initiation, possibly as genetic instruments for schizophrenia are stronger than for cannabis initiation.

The CHRNA5-A3-B4 Gene Cluster and Smoking: From Discovery to Therapeutics

Genome-wide association studies (GWASs) have identified associations between the CHRNA5-CHRNA3-CHRNB4 gene cluster and smoking heaviness and nicotine dependence. Studies in rodents have described the anatomical localisation and function of the nicotinic acetylcholine receptors (nAChRs) formed by the subunits encoded by this gene cluster. Further investigations that complemented these studies highlighted the variability of individuals' smoking behaviours and their ability to adjust nicotine intake. GWASs of smoking-related health outcomes have also identified this signal in the CHRNA5-CHRNA3-CHRNB4 gene cluster. This insight underpins approaches to strengthen causal inference in observational data. Combining genetic and mechanistic studies of nicotine dependence and smoking heaviness may reveal novel targets for medication development. Validated targets can inform genetic therapeutic interventions for smoking cessation and tobacco-related diseases.

Body mass index and psychiatric disorders: a Mendelian randomization study

Obesity is a highly prevalent risk factor for cardiometabolic diseases. Observational studies suggest that obesity is associated with psychiatric traits, but causal inference from such studies has several limitations. We used two-sample Mendelian randomization methods (inverse variance weighting, weighted median and MR-Egger regression) to evaluate the association of body mass index (BMI) with three psychiatric traits using data from the Genetic Investigation of Anthropometric Traits and Psychiatric Genomics consortia. Causal odds ratio estimates per 1-standard deviation increment in BMI ranged from 0.88 (95% CI: 0.62; 1.25) to 1.23 (95% CI: 0.65; 2.31) for bipolar disorder; 0.93 (0.78; 1.11) to 1.41 (0.87; 2.27) for schizophrenia; and 1.15 (95% CI: 0.92; 1.44) to 1.40 (95% CI: 1.03; 1.90) for major depressive disorder. Analyses removing potentially influential SNPs suggested that the effect estimates for depression might be underestimated. Our findings do not support the notion that higher BMI increases risk of bipolar disorder and schizophrenia. Although the point estimates for depression were consistent in all sensitivity analyses, the overall statistical evidence was weak. However, the fact that SNP-depression associations were estimated in relatively small samples reduced power to detect causal effects. This should be re-addressed when SNP-depression associations from larger studies become available.