PW, AJK, PS, and MAK are shareholders of Brainshake Ltd, a startup company offering NMR-based metabolite profiling. SB has received research funding from Abbott, Abbott Diagnostics, Bayer, Boehringer Ingelheim, SIEMENS, and Thermo Fisher. SB has received honoraria for lectures from Abbott, Abbott Diagnostics, Astra Zeneca, Bayer, Boehringer Ingelheim, Medtronic, Pfizer, Roche, SIEMENS Diagnostics, SIEMENS, Thermo Fisher, and as member of Advisory Boards and for consulting for Boehringer Ingelheim, Bayer, Novartis, Roche, and Thermo Fisher. GDS is a member of the Editorial Board of
Conceived and designed the experiments: PW QW AJK MT TT PS GDS MAK. Performed the experiments: AJK MT TT PS MAK. Analyzed the data: PW QW. Contributed reagents/materials/analysis tools: AJK RCR MT TT PS ASH MKa MJS SB TZ PE KPH SR VS OTR MRJ GDS MAK. Wrote the first draft of the manuscript: PW AJK RCR JS GDS MAK. Wrote the paper: PW QW AJK RCR JS MT TT PS ASH MKa JSV MJS MKä TL SM SB TZ JL AP PM MV PE KHP SR VS OTR MRJ GDS MAK. Agree with manuscript results and conclusions: PW QW AJK RCR JS MT TT PS ASH MKa JSV MJS MKä TL SM SB TZ JL AP PM MV PE KHP SR VS OTR MRJ GDS MAK. Enrolled patients: JSV MKä TL SM JL AP PM MV PE KHP VS OTR MRJ. All authors meet ICMJE criteria for authorship.
In this study, Wurtz and colleagues investigated to what extent elevated body mass index (BMI) within the normal weight range has causal influences on the detailed systemic metabolite profile in early adulthood using Mendelian randomization analysis.
Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood.
We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16–39 y; 51% women; mean ± standard deviation BMI 24±4 kg/m2). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (
Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood.
Adiposity—having excessive body fat—is a growing global threat to public health. Body mass index (BMI, calculated by dividing a person's weight in kilograms by their height in meters squared) is a coarse indicator of excess body weight, but the measure is useful in large population studies. Compared to people with a lean body weight (a BMI of 18.5–24.9 kg/m2), individuals with higher BMI have an elevated risk of developing life-shortening cardiometabolic diseases—cardiovascular diseases that affect the heart and/or the blood vessels (for example, heart failure and stroke) and metabolic diseases that affect the cellular chemical reactions that sustain life (for example, diabetes). People become unhealthily fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So adiposity can be prevented and reversed by eating less and exercising more.
Epidemiological studies, which record the patterns of risk factors and disease in populations, suggest that the illness and death associated with excess body weight is partly attributable to abnormalities in how individuals with high adiposity metabolize carbohydrates and fats, leading to higher blood sugar and cholesterol levels. Further, adiposity is also associated with many other deviations in the metabolic profile than these commonly measured risk factors. However, epidemiological studies cannot prove that adiposity causes specific changes in a person's systemic (overall) metabolic profile because individuals with high BMI may share other characteristics (confounding factors) that are the actual causes of both adiposity and metabolic abnormalities. Moreover, having a change in some aspect of metabolism could also lead to adiposity, rather than vice versa (reverse causation). Importantly, if there is a causal effect of adiposity on cardiometabolic risk factor levels, it might be possible to prevent the progression towards cardiometabolic diseases by weight loss. Here, the researchers use “Mendelian randomization” to examine whether increased BMI within the normal and overweight range is causally influencing the metabolic risk factors from many biological pathways during early adulthood. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. Several gene variants are known to lead to modestly increased BMI. Thus, an investigation of the associations between these gene variants and risk factors across the systemic metabolite profile in a population of healthy individuals can indicate whether higher BMI is causally related to known and novel metabolic risk factors and higher cardiometabolic disease risk.
The researchers measured the BMI of 12,664 adolescents and young adults (average BMI 24.7 kg/m2) living in Finland and the blood levels of 82 metabolites in these young individuals at a single time point. Statistical analysis of these data indicated that elevated BMI was adversely associated with numerous cardiometabolic risk factors. For example, elevated BMI was associated with raised levels of low-density lipoprotein, “bad” cholesterol that increases cardiovascular disease risk. Next, the researchers used a gene score for predisposition to increased BMI, composed of 32 gene variants correlated with increased BMI, as an “instrumental variable” to assess whether adiposity causes metabolite abnormalities. The effects on the systemic metabolite profile of a 1-kg/m2 increment in BMI due to genetic predisposition closely matched the effects of an observed 1-kg/m2 increment in adulthood BMI on the metabolic profile. That is, higher levels of adiposity had causal effects on the levels of numerous blood-based metabolic risk factors, including higher levels of low-density lipoprotein cholesterol and triglyceride-carrying lipoproteins, protein markers of chronic inflammation and adverse liver function, impaired insulin sensitivity, and elevated concentrations of several amino acids that have recently been linked with the risk for developing diabetes. Elevated BMI also causally led to lower levels of certain high-density lipoprotein lipids in the blood, a marker for the risk of future cardiovascular disease. Finally, an examination of the metabolic changes associated with changes in BMI in 1,488 young adults after a period of six years showed that those metabolic measures that were most strongly associated with BMI at a single time point likewise displayed the highest responsiveness to weight change over time.
These findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults beyond the effects on cholesterol and blood sugar. Like all Mendelian randomization studies, the reliability of the causal association reported here depends on several assumptions made by the researchers. Nevertheless, these findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults. Importantly, the results of both the causal effect analyses and the longitudinal study suggest that there is no threshold below which a BMI increase does not adversely affect the metabolic profile, and that a systemic metabolic profile linked with high cardiometabolic disease risk that becomes established during early adulthood can be reversed. Overall, these findings therefore highlight the importance of weight reduction as a key target for metabolic risk factor control among young adults.
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The Computational Medicine Research Team of the University of Oulu has a webpage that provides further information on
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The
The US Centers for Disease Control and Prevention has information on all aspects of
MedlinePlus provides links to other sources of information on
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The prevalence of overweight and obesity has reached epidemic proportions and represents a major threat to public health worldwide
The causal influence of adiposity on levels of metabolic risk markers can be examined within the framework of Mendelian randomization, an instrumental variable approach that uses genetic variation as an instrument to infer causality (
The principles of Mendelian randomization and core assumptions for the genetic instrument to be valid are detailed in
Mendelian randomization is an instrumental variable approach to infer causality in observational studies in the presence of potential confounding and reverse causation
(1)The association between BMI and metabolite concentration is examined in a traditional cross-sectional study design (indicated schematically by the bi-directional black arrows in
(2)The gene score is confirmed to be associated with BMI (dark red arrow in
(3)The association between the gene score and each metabolite is tested (orange arrows in
If adiposity exerts causal, non-confounded effects on a metabolite level, then the causal estimate is expected to be of a magnitude similar to that observed in the cross-sectional analysis. The causal estimate βIV and the cross-sectional association βBMI-Metabolite are compared using a
The overall correspondence between the causal effects and observational associations are summarized in
The core assumptions for the instrument to be valid for estimating causal effects by Mendelian randomization are as follows:
The gene score is robustly associated with observed BMI (
The gene score is independent of confounding factors. Associations of the gene score with age, sex, smoking, alcohol intake, physical activity, and socio-economic status are shown in
The gene score is related to the metabolite levels only through the effect on adiposity. Potential pleiotropy is assessed in
All of the associations examined are linear and not affected by interactions. The linearity of the BMI–metabolite associations is illustrated in
Clinical Characteristic | NFBC86 | NFBC66 | YFS | FINRISK 1997 |
Number of participants (women/men) | 3,976 (1,997/1,979) | 4,671 (2,321/2,350) | 2,171 (1,155/1,016) | 1,846 (995/851) |
Age (y) | 16 (—) | 31 (—) | 31.9 (4.9) | 32.3 (4.5) |
BMI (kg/m2) | 21.2 (3.4) | 24.6 (4.0) | 25.0 (4.4) | 24.7 (4.0) |
Systolic blood pressure (mm Hg) | 116 (13) | 125 (13) | 117 (13) | 125 (14) |
Total cholesterol (mmol/l) | 4.2 (0.9) | 5.3 (1.2) | 5.0 (1.0) | 5.0 (1.0) |
HDL cholesterol (mmol/l) | 1.5 (0.3) | 1.7 (0.4) | 1.6 (0.4) | 1.6 (0.3) |
Triglycerides (mmol/l) | 0.9 [0.7–1.1] | 1.0 [0.7–1.4] | 1.1 [0.9–1.6] | 0.9 [0.7–1.3] |
Plasma glucose (mmol/l) | 5.0 [4.7–5.2] | 5.0 [4.7–5.3] | 5.0 [4.7–5.3] | 4.8 [4.5–5.1] |
Insulin (IU/l) | 9.5 [7.3–12] | 7.5 [6.2–9.4] | 6 |
4.7 [3.3–6.6] |
Physical activity index (h/wk) | 30 |
11 |
13 |
— |
Alcohol usage (g/d) | — | 4 |
5 [0–15] | 4 [0–11] |
Smoking prevalence (percent) | 12% (11–13) | 40% (39–42) | 24% (22–26) | 28% (26–30) |
Prevalence of overweight (percent) | 9% (8–10) | 31% (30–32) | 32% (30–33) | 32% (30–34) |
Prevalence of obesity (percent) | 3% (2–3) | 8% (7–9) | 12% (11–13) | 9% (8–10) |
Association of gene score for elevated BMI with observed BMI (β ± standard error, kg/m2) | 0.91±0.10 | 1.21±0.11 | 0.92±0.17 | 1.14±0.17 |
Variation in observed BMI explained by the gene score for elevated BMI | 2.2% | 2.6% | 1.3% | 2.1% |
Values are mean (SD), median [interquartile range], or percentage (95% confidence interval) for normally distributed, skewed, and categorical variables, respectively. The gene score for predisposition to elevated BMI was derived based on weighting each genetic variant in the score by effects established previously in genome-wide meta-analysis
rs Number, Effect Allele/Other Allele | Nearest Gene | Weight in Gene Score | Effect Allele Frequency | Possible Proxy in NFBC86 (*) or in FINRISK (**) | Association with BMI in This Study, β (SE); |
Correspondence between Causal and Observed Effect Estimates without Pertinent Variant in the Gene Score |
rs1558902, A/T | 0.39 | 39.9 | rs1421085* (LD = 1.00) | 0.110 (0.016); |
Slope = 0.79±0.042 | |
rs9939609** (LD = 0.90) | Intercept = −0.0063 | |||||
rs2867125, C/T | 0.31 | 84.3 | 0.063 (0.017); |
Slope = 0.87±0.034 | ||
Intercept = −0.00076 | ||||||
rs571312, A/C | 0.23 | 17.9 | 0.098 (0.016); |
Slope = 0.87±0.036 | ||
Intercept = −0.0017 | ||||||
rs10938397, G/A | 0.18 | 50.4 | rs1264198* (LD = 0.97) | 0.043 (0.013); |
Slope = 0.88±0.039 | |
Intercept = −0.0028 | ||||||
rs10767664, A/T | 0.19 | 82.7 | rs2030323* (LD = 1.00) | 0.041 (0.022); |
Slope = 0.87±0.034 | |
Intercept = −0.003 | ||||||
rs2815752, A/G | 0.13 | 64.5 | 0.037 (0.019); |
Slope = 0.82±0.033 | ||
Intercept = 7.6×10−5 | ||||||
rs7359397, T/C | 0.15 | 41.6 | 0.033 (0.022); |
Slope = 0.88±0.034 | ||
Intercept = −0.0038 | ||||||
rs9816226, T/A | 0.14 | 84.5 | rs7647305* (LD = 0.72) | 0.020 (0.017); |
Slope = 0.85±0.036 | |
Intercept = −0.003 | ||||||
rs3817334, T/C | 0.06 | 39.7 | rs10838738** (LD = 0.84) | 0.020 (0.014); |
Slope = 0.85±0.034 | |
Intercept = 0.00081 | ||||||
rs29941, G/A | 0.06 | 60.6 | rs11084753** (LD = 0.65) | 0.021 (0.013); |
Slope = 0.9±0.033 | |
Intercept = −0.0023 | ||||||
rs543874, G/A | 0.22 | 17.7 | 0.100 (0.016); |
Slope = 0.89±0.035 | ||
Intercept = −0.0029 | ||||||
rs987237, G/A | 0.13 | 20.4 | Missing** | 0.094 (0.017); |
Slope = 0.84±0.037 | |
Intercept = −0.0021 | ||||||
rs7138803, A/G | 0.12 | 36.6 | 0.046 (0.013); |
Slope = 0.9±0.034 | ||
Intercept = −0.0015 | ||||||
rs10150332, C/T | 0.13 | 23.1 | rs17109256* (LD = 1.00) | 0.052 (0.032); |
Slope = 0.86±0.035 | |
Intercept = −0.0019 | ||||||
rs713586, C/T | 0.14 | 42.9 | rs10182181* (LD = 1.00) | 0.056 (0.014); |
Slope = 0.88±0.034 | |
Missing** | Intercept = −0.0025 | |||||
rs12444979, C/T | 0.17 | 87.5 | 0.061 (0.019); |
Slope = 0.86±0.034 | ||
Intercept = −0.0016 | ||||||
rs2241423, G/A | 0.13 | 84.5 | 0.025 (0.017); |
Slope = 0.87±0.034 | ||
Intercept = −0.0027 | ||||||
rs2287019, C/T | 0.15 | 78.0 | Missing** | 0.020 (0.016); |
Slope = 0.88±0.035 | |
Intercept = −0.004 | ||||||
rs1514175, A/G | 0.07 | 48.9 | 0.052 (0.013); |
Slope = 0.89±0.036 | ||
Intercept = −0.00083 | ||||||
rs13107325, T/C | 0.19 | 1.2 | 0.076 (0.058); |
Slope = 0.86±0.035 | ||
Intercept = −0.0019 | ||||||
rs2112347, T/G | 0.10 | 60.3 | 0.023 (0.019); |
Slope = 0.89±0.034 | ||
Intercept = −0.0013 | ||||||
rs10968576, G/A | 0.11 | 39.3 | 0.031 (0.016); |
Slope = 0.86±0.036 | ||
Intercept = −0.0022 | ||||||
rs3810291, A/G | 0.09 | 64.0 | 0.038 (0.014); |
Slope = 0.86±0.034 | ||
Intercept = −0.0017 | ||||||
rs887912, T/C | 0.10 | 26.2 | 0.024 (0.014); |
Slope = 0.87±0.035 | ||
Intercept = −0.0019 | ||||||
rs13078807, G/A | 0.10 | 16.0 | 0.059 (0.017); |
Slope = 0.87±0.034 | ||
Intercept = −0.0021 | ||||||
rs11847697, T/C | 0.17 | 1.3 | rs10134820* (LD = 0.74) | 0.027 (0.054); |
Slope = 0.86±0.034 | |
Intercept = −0.0022 | ||||||
rs2890652, C/T | 0.09 | 21.8 | rs17834293* (LD = 0.70) | 0.017 (0.025); |
Slope = 0.87±0.035 | |
Intercept = −0.0018 | ||||||
rs1555543, C/A | 0.06 | 59.3 | rs11165643* (LD = 1.00) | 0.015 (0.013); |
Slope = 0.87±0.034 | |
Intercept = −0.0023 | ||||||
rs4771122, G/A | 0.09 | 30.7 | rs1006353* (LD = 0.74) | 0.030 (0.015); |
Slope = 0.86±0.034 | |
Missing** | Intercept = −0.0022 | |||||
rs4836133, A/C | 0.07 | 48.7 | rs6864049* (LD = 1.00) | −0.003 (0.020); |
Slope = 0.86±0.034 | |
Intercept = −0.0022 | ||||||
rs4929949, C/T | 0.06 | 53.9 | rs7127684* (LD = 1.00) | 0.031 (0.016); |
Slope = 0.87±0.034 | |
Intercept = −0.0022 | ||||||
rs206936, G/A | 0.06 | 22.7 | 0.026 (0.015); |
Slope = 0.87±0.035 | ||
Intercept = −0.0023 | ||||||
Weights of the individual genetic variants are based on prior genome-wide analyses
LD, linkage disequilibrium; SE, standard error.
Intervention trials have shown favorable effects of weight reduction on cardiovascular risk factors
The study comprised four population-based cohorts (
A high-throughput serum nuclear magnetic resonance (NMR) spectroscopy platform
A gene score for predisposition to elevated BMI, composed of 32 single nucleotide polymorphisms firmly associated with BMI in prior genome-wide association studies, was used as the instrument to assess causality
Metabolites with skewed distributions were log-transformed prior to analyses. All metabolite concentrations were scaled to standard deviation (SD) units separately in each cohort. This scaling enabled comparison of association magnitudes across the metabolic measures. We calculated that 33 principal components explain>95% of the variance in each cohort
For cross-sectional analyses, linear regression models were fitted for each metabolite, with BMI as the explanatory variable and the metabolite concentration as outcome. The regression coefficients βBMI–Metabolite were calculated in units of 1-SD metabolite concentration per 1-kg/m2 increment in BMI. Associations were adjusted for sex and age, if applicable. Results were analyzed separately for the four cohorts and combined using random effect inverse-variance-weighted meta-analysis
For Mendelian randomization analyses, we used a gene score for predisposition to higher BMI as the instrumental variable. The individual genetic variants and their weights in the gene score are listed in
Changes in metabolite levels with changes in BMI over time were examined for 1,488 individuals for whom metabolite data were also available at 6-y follow-up. Longitudinal associations were assessed for each metabolite using a linear regression model with the 6-y change in BMI as predictor and the 6-y change in metabolite concentration as the outcome. The models were adjusted for age and sex. No robust sex interactions were observed in longitudinal analyses. To enable comparison with the cross-sectional associations, longitudinal association magnitudes are reported as the change in metabolite concentration (in units of baseline SD) per unit change in BMI during follow-up. Longitudinal association magnitudes were then tested for statistical difference from the corresponding cross-sectional associations in the full study population. The overall correspondence between the association patterns was quantified by a linear fit of longitudinal versus cross-sectional associations. The longitudinal analyses were additionally replicated for 1,372 individuals at 10-y follow-up in YFS, as well as in the Pieksämäki Study, consisting of 456 middle-aged persons with 6-y follow-up (
The study comprised 12,664 adolescents and young adults from four general population cohorts who all had detailed metabolite profiles measured and information on gene score for predisposition to elevated BMI. In addition, 1,488 individuals also had metabolite profiling data at 6-y follow-up. Clinical characteristics of the four cohorts are shown in
Cross-sectional associations of BMI with 82 metabolic measures are illustrated in
Association magnitudes are indicated in units of 1-SD metabolite concentration per 1-kg/m2 increment in BMI. Associations were adjusted for age and meta-analyzed for the four cohorts of adolescents and young adults. Colored dots indicate β-regression coefficients, colored shading denotes 95% confidence intervals, and boundaries on the shading indicate
Most metabolite associations followed approximately linear shapes across the range of BMI, with increases observed already within the normal weight range (BMI<25 kg/m2), as illustrated in
Association magnitudes are in units of 1-SD metabolite concentration per 1-kg/m2 increment in BMI. Color coding indicates the respective cohorts. White dots indicate β-regression coefficients, colored shading indicates 95% confidence intervals, and darker shading denotes
The causal effects of BMI on the systemic metabolite profile were analyzed using Mendelian randomization. The principles of this instrumental variable framework are detailed in
Causal effect estimates of BMI on the 82 metabolic measures are shown by the orange bars in
Association magnitudes are in units of 1-SD metabolite concentration per 1-kg/m2 increment in BMI (cross-sectional associations [white] and causal effect estimates [orange];
Causal effect estimates based on Mendelian randomization are plotted against the metabolite associations with observed BMI based a cross-sectional study design. The orange dashed line denotes the linear fit of the correspondence. Darker dots indicate statistical differences between causal effect estimates and cross-sectional association magnitudes. The gray shaded areas serve to guide the eye for the slope of correspondence. BP, blood pressure; PUFA, polyunsaturated fatty acid; SHBG, sex hormone–binding globulin.
In terms of individual metabolic risk factors, the causal estimates were significant for 24 of the metabolic measures at
Causal effect estimates were similar when using an unweighted gene score as the instrument for Mendelian randomization analyses (slope 0.82±0.04;
To study the response of the metabolite profile to weight change, we examined associations between change in BMI and change in metabolite levels among 1,488 young adults at 6-y follow-up. These longitudinal associations are illustrated in
The green dashed line denotes the linear fit between longitudinal and cross-sectional observations. Darker dots indicate statistical differences between longitudinal and cross-sectional association magnitudes. The gray shaded areas serve to guide the eye for the slope of correspondence. BP, blood pressure; MUFA, monounsaturated fatty acid; SHBG, sex hormone–binding globulin.
Larger metabolic changes than expected based on the cross-sectional associations were observed for numerous lipoprotein lipid and cholesterol measures, fatty acids, and branched-chain amino acids, as well as inflammatory markers, adiponectin, and insulin. The magnitudes of the longitudinal associations in absolute concentration units are listed in
Changes in the metabolite profile with weight loss and weight gain at 6-y follow-up are illustrated in
Median changes in metabolite concentrations at 6-y follow-up in four categories of weight change: filled gray bars, 6%–10% weight loss (mean [SD] loss 5.5±1.1 kg,
In this study of 12,664 healthy adolescents and young adults, elevated BMI was associated with adverse effects on numerous known and novel risk markers for cardiovascular disease and type 2 diabetes throughout the systemic metabolite profile
The causal effects of adiposity across multiple metabolic measures corroborate prior Mendelian randomization studies, which have examined the role of the
Despite the heritable component of adiposity, BMI is a modifiable risk factor. Changes in BMI were paralleled by changes throughout the metabolite profile (
Our study has both strengths and limitations. BMI is a heterogeneous marker of adiposity; however, it predicts the risk of related complications and is relevant for large population studies
The ideal body weight that healthy adults should strive to attain remains controversial
Correlations of the assayed metabolic measures.
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Metabolite concentrations as a function of BMI on a continuous scale in young women and men.
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Correspondence between gene score associations and cross-sectional associations of metabolic measures when the gene score associations are adjusted for observed BMI.
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Associations of metabolite changes with change in BMI at 6-y and 10-y follow-up in the Cardiovascular Risk in Young Finns Study, and at 6-y follow-up in the Pieksämäki Study.
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Mean (SD) metabolite concentrations in each cohort and conversion factors to absolute units.
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Correlations between the gene score for elevated BMI and potential confounders.
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Cross-sectional associations, causal effect estimates, and longitudinal associations of BMI with systemic metabolites in absolute concentration units.
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Suggestive sex interactions in causal effect estimates of BMI on metabolites.
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Metabolite changes paralleled by weight loss and weight gain during 6-y follow-up in absolute concentration units.
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Study populations.
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body mass index
high-density lipoprotein
low-density lipoprotein
Northern Finland Birth Cohort of 1966
Northern Finland Birth Cohort of 1986
nuclear magnetic resonance
standard deviation
very-low-density lipoprotein
Cardiovascular Risk in Young Finns Study