Transcript Supplementary Table 1 - 埼玉医科大学総合医療センター 内分泌

Journal Club
Wirth CD, Blum MR, da Costa BR, Baumgartner C, Collet TH, Medici M, Peeters RP,
Aujesky D, Bauer DC, Rodondi N.
Subclinical Thyroid Dysfunction and the Risk for Fractures: A Systematic Review and
Meta-analysis.
Ann Intern Med. 2014 Aug 5;161(3):189-99. doi: 10.7326/M14-0125.
Scott RA, Fall T, Pasko D, Barker A, Sharp SJ, Arriola L, Balkau B, Barricarte A, Barroso I, Boeing H, Clavel-Chapelon F, Crowe
FL, Dekker JM, Fagherazzi G, Ferrannini E, Forouhi NG, Franks PW, Gavrila D, Giedraitis V, Grioni S, Groop LC, Kaaks R, Key
TJ, Kühn T, Lotta LA, Nilsson PM, Overvad K, Palli D, Panico S, Quirós JR, Rolandsson O, Roswall N, Sacerdote C, Sala N,
Sánchez MJ, Schulze MB, Siddiq A, Slimani N, Sluijs I, Spijkerman AM, Tjonneland A, Tumino R, van der A DL, Yaghootkar H;
The RISC The study group InterAct consortium, McCarthy MI, Semple RK, Riboli E, Walker M, Ingelsson E, Frayling TM,
Savage DB, Langenberg C, Wareham NJ.
Common genetic variants highlight the role of insulin resistance and body fat
distribution in type 2 diabetes, independently of obesity.
Diabetes. 2014 Jun 19. pii: DB_140319. [Epub ahead of print]
2014年8月21日 8:30-8:55
８階 医局
埼玉医科大学 総合医療センター 内分泌・糖尿病内科
Department of Endocrinology and Diabetes,
Saitama Medical Center, Saitama Medical University
松田 昌文
Matsuda, Masafumi
University of Bern, Bern, Switzerland; University Hospital of Lausanne,
Lausanne, Switzerland; Erasmus Medical Center, Rotterdam, the Netherlands;
and University of California, San Francisco, San Francisco, California.
Ann Intern Med. 2014;161:189-199. doi:10.7326/M14-0125
Background: Data on the association
between subclinical thyroid dysfunction and
fractures conflict.
Purpose: To assess the risk for hip and
nonspine fractures associated with
subclinical thyroid dysfunction among
prospective cohorts.
Data Sources: Search of MEDLINE and
EMBASE (1946 to 16 March 2014) and reference
lists of retrieved articles without language
restriction.
Study Selection: Two physicians screened and
identified prospective cohorts that measured
thyroid function and followed participants to
assess fracture outcomes.
Data Extraction: One reviewer extracted data
using a standardized protocol, and another
verified data. Both reviewers independently
assessed methodological quality of the studies.
Thyroid hormones influence the homeostasis and remodeling of
bone (8). Overt hyperthyroidism is a risk factor for fractures (9). A
few observational studies have also found an increased risk for
fracture in persons with overt hypothyroidism (10, 11).
8. Waung JA, Bassett JH, Williams GR. Thyroid hormone metabolism in skeletal development and
adult bone maintenance. Trends Endocrinol Metab. 2012; 23:155-62. [PMID: 22169753]
9. Vestergaard P, Mosekilde L. Hyperthyroidism, bone mineral, and fracture risk—a meta-analysis.
Thyroid. 2003;13:585-93. [PMID: 12930603]
10. Ahmed LA, Schirmer H, Berntsen GK, Fønnebø V, Joakimsen RM. Features of the metabolic
syndrome and the risk of non-vertebral fractures: the Tromsø study. Osteoporos Int. 2006;17:42632. [PMID: 16437192]
11. Vestergaard P, Rejnmark L, Mosekilde L. Influence of hyper- and hypothyroidism, and the
effects of treatment with antithyroid drugs and levothyroxine on fracture risk. Calcif Tissue Int.
2005;77:139-44. [PMID: 16151671]
12. Lee JS, Buzkova´ P, Fink HA, Vu J, Carbone L, Chen Z, et al. Subclinical thyroid dysfunction
and incident hip fracture in older adults. Arch Intern Med. 2010;170:1876-83. [PMID: 21098345]
13. Waring AC, Harrison S, Fink HA, Samuels MH, Cawthon PM, Zmuda JM, et al; Osteoporotic
Fractures in Men (MrOS) Study. A prospective study of thyroid function, bone loss, and fractures in
older men: The MrOS study. J Bone Miner Res. 2013;28:472-9. [PMID: 23018684]
14. Flynn RW, Bonellie SR, Jung RT, MacDonald TM, Morris AD, Leese GP. Serum thyroidstimulating hormone concentration and morbidity from cardiovascular disease and fractures in
patients on long-term thyroxine therapy. J Clin Endocrinol Metab. 2010;95:186-93. [PMID:
19906785]
Data Synthesis: The 7 population-based cohorts of
heterogeneous quality included 50 245 participants with 1966 hip
and 3281 nonspine fractures. In random-effects models that
included the 5 higher-quality studies, the pooled adjusted hazard
ratios (HRs) of participants with subclinical hyperthyroidism versus
euthyrodism were 1.38 (95% CI, 0.92 to 2.07) for hip fractures and
1.20 (CI, 0.83 to 1.72) for nonspine fractures without statistical
heterogeneity (P = 0.82 and 0.52, respectively; I2 = 0%). Pooled
estimates for the 7 cohorts were 1.26 (CI, 0.96 to 1.65) for hip
fractures and 1.16 (CI, 0.95 to 1.42) for nonspine fractures. When
thyroxine recipients were excluded, the HRs for participants with
subclinical hyperthyroidism were 2.16 (CI, 0.87 to 5.37) for hip
fractures and 1.43 (CI, 0.73 to 2.78) for nonspine fractures. For
participants with subclinical hypothyroidism, HRs from higherquality studies were 1.12 (CI, 0.83 to 1.51) for hip fractures and
1.04 (CI, 0.76 to 1.42) for nonspine fractures (P for
heterogeneity = 0.69 and 0.88, respectively; I2 = 0%).
Limitations: Selective reporting cannot be
excluded. Adjustment for potential common
confounders varied and was not adequately done
across all studies.
Conclusion: Subclinical hyperthyroidism might
be associated with an increased risk for hip and
nonspine fractures, but additional large, highquality studies are needed.
Primary Funding Source: Swiss National
Science Foundation.
Message
潜在性甲状腺機能亢進症に関連する股関節/非脊
椎骨折リスクを、集団ベースのコホート研究7件
（被験者約5万人、股関節骨折1966件、非脊椎骨
折3281件）のシステマティックレビューとメタ
解析で検証。質の高い研究5件での機能正常群に
対する患者群の骨折ハザード比は股関節1.38、
非脊椎1.20だった（Ｐ＝0.82、0.52）。
甲状腺機能低下でも骨折は増加しそうだが。いずれ
にしても、有意差はないようである。
Diabetes. 2014 Jun 19. pii: DB_140319. [Epub ahead of print]
(1) MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom, (2) Department of Medical
Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden, (3)
Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK, (4) Public Health Division of
Gipuzkoa, San Sebastian, Spain, (5) Instituto BIO-Donostia, Basque Government, San Sebastian, Spain, (6)
CIBER Epidemiología y Salud Pública (CIBERESP), Spain, (7) Inserm, CESP, U1018, Villejuif, France, (8) Univ
Paris-Sud, UMRS 1018, Villejuif, France, (9) Navarre Public Health Institute (ISPN), Pamplona, Spain, (10) The
Wellcome Trust Sanger Institute, Cambridge, United Kingdom, (11) University of Cambridge Metabolic Research
Laboratories, Cambridge, UK, (12) German Institute of Human Nutrition Potsdam-Rehbruecke, Germany, (13)
University of Oxford, United Kingdom, (14) Department of Epidemiology and Biostatistics, VrijeUniversiteit Medical
Center, Amsterdam, The Netherlands, (15) Department of Internal Medicine, University of Pisa, Pisa, Italy, (16)
Lund University, Malmö, Sweden, (17) Umeå University, Umeå, Sweden, (18) Department of Epidemiology, Murcia
Regional Health Council, Murcia, Spain, (19) Department of Public Health and Caring Sciences, Geriatrics,
Uppsala University Sweden, (20) Epidemiology and Prevention Unit, Milan, Italy, (21) University Hospital Scania,
Malmö, Sweden, (22) Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland, (23)
German1 Cancer Research Centre (DKFZ), Heidelberg, Germany, (24) 1 Department of Public Health, Section for
Epidemiology, Aarhus University, Aarhus, Denmark, (25) Aalborg University Hospital, Aalborg, Denmark, (26)
Cancer Research and Prevention Institute (ISPO), Florence, Italy, (27) Dipartimento di Medicina Clinica e
Chirurgia, Federico II University, Naples, Italy, (28) Public Health Directorate, Asturias, Spain, (29) Danish Cancer
Society Research Center, Copenhagen, Denmark, (30) Unit of Cancer Epidemiology, Citta' della Salute e della
Scienza Hospital-University of Turin and Center for Cancer Prevention (CPO), Torino, Italy, (31) Human Genetics
Foundation (HuGeF), Torino, Italy, (32) Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research
Program, and Translational Research Laboratory, Catalan Institute of Oncology (IDIBELL), Barcelona, Spain, (33)
Andalusian School of Public Health, Granada, Spain, (34) Instituto de Investigación Biosanitaria de Granada
(Granada.ibs), Granada (Spain), (35) School of Public Health, Imperial College London, UK, (36) International
Agency for Research on Cancer, Lyon, France, (37) University Medical Center Utrecht, Utrecht, the Netherlands,
(38) National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands, (39) ASP
Ragusa, Italy, (40) Aire Onlus, Ragusa, Italy, (41) Oxford Centre for Diabetes, Endocrinology and Metabolism
(OCDEM), University of Oxford, UK, (42) Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford,
UK, (43) Oxford NIHR Biomedical Research Centre, Oxford, UK, (44) Institute of Cellular Medicine, Newcastle
University, Newcastle upon Tyne, UK
Aims/Hypothesis:
We aimed to validate genetic variants as
instruments for insulin resistance and secretion,
to characterise their association with intermediate
phenotypes, and to investigate their role in T2D
risk among normal-weight, overweight and obese
individuals.
Methods:
We investigated the association of genetic scores
with euglycaemic-hyperinsulinaemic clamp- and
OGTT-based measures of insulin resistance and
secretion, and a range of metabolic measures in
up to 18,565 individuals. We also studied their
association with T2D risk among normal-weight,
overweight and obese individuals in up to 8,124
incident T2D cases.
The Medical Research Council Ely Prospective Study.
Supplementary Figure 1:
Regional compartment definition in
the Fenland DXA data.
The trunk region extends from the chin
to the top of the pelvis. The leg regions
are defined by a cut across the femoral
neck, not touching the pelvis. The arm
regions are defined by a cut placed as
close to the body as possible. The
android region is a quadrilateral box,
where the lower boundary is the pelvis,
the lateral boundaries are the arm cuts
and sthe upper boundary is above the
pelvis cut by 20% of the distance
between the pelvis and neck cuts. The
gynoid region is another quadrilateral
box where the lateral boundaries are
the arm cuts. The upper boundary is
below the pelvis cut by 1.5 x the height
of the android region. The Lower
boundary is below the upper boundary
by 2 x the height of the android region.
Genetic risk scores
We created unweighted (i.e. per-allele) genetic risk scores for insulin resistance and
impaired insulin secretion using effect alleles defined from the literature as shown in
Supplementary Table 1.
The insulin resistance genetic score comprised variants associated with fasting insulin in
recent meta-analyses (8). In order to improve specificity we restricted the insulin
resistance score to the 10 variants showing association (p<0.05) with lower HDL and
higher triglycerides (8,16): a hallmark of common insulin resistance. This excluded
TCF7L2, associated principally with insulin secretion (17), and FTO, whose effect on
insulin levels was entirely mediated by BMI (8). Variants included were those in or near
the IRS1, GRB14, ARL15, PPARG, PEPD, ANKRD55/MAP3K1, PDGFC, LYPLAL1,
RSPO3, and FAM13A1 genes (Supplementary Table 1).
For the insulin secretion score, from loci associated with T2D and related traits (18–21)
we undertook literature searches to identify SNPs showing an association with impaired
early insulin secretion. In addition, we investigated the literature to identify additional
candidate genes associated with early insulin secretion. Up to 21 variants associated
with decreased early insulin secretion were included in the insulin secretion score.
Where SNPs were missing, we included a proxy where available (Supplementary Table
1), and where no proxy was available, we did not impute missing SNPs. Each SNP, the
reason for inclusion in the score and its availability in each study is shown in
Supplementary Table 1. The genetic score distributions in each study are shown in for
the insulin secretion and insulin resistance scores in Supplementary Figure 2a and b,
respectively.
Supplementary Table 1: SNPs included in the genetic risk scores, with the study
in which they were identified or implicated in insulin secretion or resistance. The
risk allele for each of these SNPs is also shown. Where the lead SNP was not
available, the proxy used is listed for each study. Where a suitable proxy was not
available, the SNP is marked “x”.
insulin resistance
Supplementary Figure 2: Histograms of genetic risk scores from each study for a)
insulin secretion and b) insulin resistance scores.
insulin secretion
Supplementary Figure 2: Histograms of genetic risk scores from each study for a)
insulin secretion and b) insulin resistance scores.
insulin resistance
Figure 1a-b: Association of the insulin resistance and secretion risk scores with a range of
standardised outcomes. Effect sizes are expressed per-risk allele. All models were adjusted for age,
sex and BMI, other than anthropometric traits, which were adjusted only for age and sex.
insulin secretion
Figure 1a-b: Association of the insulin resistance and secretion risk scores with a range of
standardised outcomes. Effect sizes are expressed per-risk allele. All models were adjusted for age,
sex and BMI, other than anthropometric traits, which were adjusted only for age and sex.
Figure 2: Association of the insulin resistance score on standardised anthropometric
traits in the Fenland study. Effect sizes are expressed per-risk allele. All models were
adjusted for age and sex.
Figure 3: Association of the risk scores with type 2 diabetes in the EPIC-InterAct study. Associations
are shown overall and by strata of BMI and waist circumference at baseline. BMI strata were
defined by WHO BMI cutoffs and waist circumference strata were defined by sex-specific tertiles
(low: M<94cm, F<78.5cm; med: M>94-103cm, F>78.5-90cm; high: M>103cm, F>90cm).
Results:
The insulin resistance score was associated with
lower insulin sensitivity measured by M/I value (β in
SDs-per10 allele [95%CI]:-0.03[-0.04,-0.01];p=0.004).
This score was associated with lower BMI (- 0.01[0.01,-0.0;p=0.02) and gluteofemoral fat-mass (-0.03[0.05,-0.02;p=1.4x10-6), and with higher ALT
(0.02[0.01,0.03];p=0.002) and gamma-GT
(0.02[0.01,0.03];p=0.001).
While the secretion score had a stronger association
with T2D in leaner individuals (pinteraction=0.001), we
saw no difference in the association of the insulin
resistance score with T2D among BMI waist-strata
(pinteraction>0.31).
Conclusions:
While insulin resistance is often considered
secondary to obesity, the association of the
insulin resistance score with lower BMI and
adiposity and with incident T2D even among
individuals of normal weight highlights the role of
insulin resistance and ectopic fat distribution in
T2D, independently of body size.
Message
リスク遺伝子自体の定義はOKか疑問もあるが、
定義されたインスリン抵抗性遺伝子リスクスコ
アとインスリン分泌遺伝子スコアと実際の表現
型との関連を確認した論文。Cambridge大学が中
心なのでHOMAは使っていないようである。
インスリン抵抗性遺伝子リスクスコアとMatsuda
indexがきちんと関連するのは、そのようにリス
クスコアが設定されていると言えばそれまでだ
が、BMIが低いことと関連しているのは？？？