Articles

Life expectancy associated with different ages at diagnosisof type 2 diabetes in high-income countries: 23 million
person-years of observation
Emerging Risk Factors Collaboration*

Summary

Background The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimatessuggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide
reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specificmortality, and reductions in life expectancy.

Lancet Diabetes Endocrinol 2023

Methods For this observational study, we conducted a combined analysis of individual-participant data from 19 highincome countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, medianbaseline years 1961–2007, median latest follow-up years 1980–2013) and the UK Biobank (median baseline year 2006,
median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause
mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths wererecorded during 23·1 million person-years of follow-up. We estimated cumulative survival by applying age-specificHRs to age-specific death rates from 2015 for the USA and the EU.

See Online/Comment
https://doi.org/10.1016/
S2213-8587(23)00255-3

Findings For participants with diabetes, we observed a linear dose–response association between earlier age at diagnosis
and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2·69 (95% CI 2·43–2·97)
when diagnosed at 30–39 years, 2·26 (2·08–2·45) at 40–49 years, 1·84 (1·72–1·97) at 50–59 years, 1·57 (1·47–1·67) at
60–69 years, and 1·39 (1·29–1·51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men andwomen. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when
diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier when diagnosed aged 50 years
than an individual without diabetes. Using EU death rates, the corresponding estimates were 13, 9, or 5 years earlier.

Published Online
September 11, 2023
https://doi.org/10.1016/
S2213-8587(23)00223-1*Members of the writing
committee are listed at the endof the Article and a full list ofinvestigators is provided in the
appendix (p 34).
Correspondence to:Prof Emanuele Di Angelantonio,
Department of Public Healthand Primary Care, Universityof Cambridge,
Cambridge CB1 8RN, UK
ed303@medschl.cam.ac.uk
See Online for appendix

Interpretation Every decade of earlier diagnosis of diabetes was associated with about 3–4 years of lower life expectancy,highlighting the need to develop and implement interventions that prevent or delay the onset of diabetes and tointensify the treatment of risk factors among young adults diagnosed with diabetes.
Funding British Heart Foundation, Medical Research Council, National Institute for Health and Care Research, andHealth Data Research UK.
Crown Copyright © 2023 Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Introduction
The prevalence of type 2 diabetes is increasing globally,
driven mainly by behavioural and societal factors related
to obesity, nutrition, and physical activity.1–3 In 2021,
537 million adults were estimated to have diabetes
worldwide, with increasing numbers diagnosed atyounger ages.3,4Previous estimates have suggested that adults with
type 2 diabetes die, on average, 6 years earlier than theircounterparts without diabetes.5–7 However, how thisaverage reduction in life expectancy varies according toage at diagnosis is uncertain.8–19 Valid characterisationof this association requires prospective comparison ofoutcomes within the same cohorts of people with diabetes
diagnosed at varying ages. However, few population
cohorts have had sufficient statistical power, detail, andduration of follow-up to enable reliable estimation.20–25
Moreover, previous modelling studies—which usedstate-transition models and life tables that rely on inputs
from aggregated data—have considered diabetes only asa binary condition (ie, absent or present) when estimating
its effect on life expectancy.7,26–29 Few published studies
have therefore directly analysed the association of age at
diagnosis of diabetes with mortality and life expectancy.17,18,25
We aimed to provide reliable estimates of the associ­ations of age at diagnosis of diabetes with all-cause
mortality, cause-specific mortality, and reductions in life
expectancy in high-income countries. We analysed indi­vidual records from 97 long-term, prospective cohorts,
involving 1 515 718 participants followed up for a total of
23·1 million person-years.

Methods

Study design, data sources, and participants
We conducted a combined analysis of individualparticipant data from two large-scale data sources,

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Research in context
Evidence before this study
We searched MEDLINE for records published in English fromdatabase inception to Nov 30, 2022, that reported on
associations of age at diagnosis or duration of diabetes withall-cause mortality and years of life lost according to age at
diagnosis. Search terms related to exposure (diabetes, diabetes
mellitus, age at diagnosis, age of onset, duration), outcomes(mortality, death, life expectancy, survival), study design (cohort
studies, cohort, prospective) and association measures (relativerisk, hazard ratio, risk ratio, rate ratio). Only a few studies haveanalysed these associations directly or had sufficient statisticalpower, detail, and duration of follow-up to enable reliable
estimation. Findings generally showed higher mortality risk
with younger age at diagnosis of type 2 diabetes, butinterpretation was limited by different categorisations used.
Added value of this study
We used information from large-scale data sources covering
19 high-income countries and containing individual records on
1 515 718 participants, in whom deaths were recorded during23·1 million person-years of follow-up. We calculated ageadjusted and sex-adjusted hazard ratios for all-cause mortality
according to age at diagnosis of type 2 diabetes and estimatedeach constituting prospective population cohort studies
with information on age at diagnosis of diabetes(appendix pp 2–3, 24). The first data source, theEmerging Risk Factors Collaboration (ERFC), is a col­laboration of prospective cohort studies withinformation about various risk factors, cardiovasculardisease outcomes, and mortality.30 Prospective cohort
studies contributing to the ERFC were included in thisanalysis if they met all the following criteria: hadrecruited participants on the basis of informed consent;
did not select participants on the basis of having
previous chronic disease (including cardiovasculardisease and diabetes); had recorded information ondiabetes status and age at diagnosis of diabetes; hadrecorded cause-specific deaths; and had accrued morethan 1 year of follow-up. The second data source was the
UK Biobank, a single, large prospec­tive study in which
participants were recruited from 22 centres throughout
the UK.31 After giving consent, participants provided
biological samples and com­pleted a touch-screen
questionnaire, a computer-assisted interview, anda physical examination (appendix p 26). Data fromparticipants in the UK Biobank have been linked withdeath records of the UK Office for National Statistics
through National Health Service identification numbers.
For all studies, written informed consent was obtained
from participants and approval was obtained fromrelevant ethics committees.
We ascertained baseline diabetes status on the basis ofself-report information, medical records, medication
usage, or a combination of these factors (appendix
2

cumulative survival by applying the age-specific estimates tocontemporary age-specific death rates. We found a steep linear
dose–response association between earlier age at diagnosis of
diabetes and higher risk of all-cause mortality. Our public healthmodelling suggested that, for individuals surviving to age
50 years, those with diabetes diagnosed aged 30 years died
14 years earlier, those diagnosed aged 40 years died 10 years
earlier, and those diagnosed aged 50 years died 6 years earlier, on
average, than individuals without diabetes—in other words, every
decade of earlier diagnosis of type 2 diabetes was associated with
about 3–4 years of reduced life expectancy. Our study provides
reliable estimates of the associations of age at diagnosis of
diabetes with all-cause mortality in high-income countries.
Implications of all the available evidence
Because earlier diagnosis of type 2 diabetes is associated withshorter life expectancy, high priority should be given todeveloping and implementing interventions that prevent ordelay the onset of the condition, especially as its prevalence
among younger age groups is increasing globally. The evidence
also highlights the need for intensive treatment of risk factors
for premature mortality among young adults diagnosed withdiabetes.

p 4).5,32 To calculate age at diagnosis of diabetes, we used
information recorded at the baseline enrolment survey
in prospective cohort studies, supplemented, whenavailable, by information on new-onset incident type 2
diabetes recorded during follow-up (appendix p 27). For
37 513 (79·1%) of 47 404 new-onset incident cases, age at
diagnosis of diabetes was calculated using the date ofdiagnosis provided by the contributing cohorts. For theremaining 9891 (20·9%) new-onset incident cases, forwhich information was provided as diabetes status (yesor no) at date-stamped resurveys, we estimated the age at
diagnosis as the participant’s age at the midpoint of thetwo consecutive surveys between which the participant
developed diabetes (appendix pp 2–3). We also computed
an accuracy indicator as the half-width of the timeinterval between the two surveys, and the average was
2·4 years (SD 0·9; appendix pp 2–3).
We classified mortality according to the primary cause(or, in its absence, the underlying cause) on the basis ofcoding from the International Classification of Diseases(revisions 8–10) to at least three digits, or according tostudy-specific classification systems. Classification ofdeaths was based on death certificates, supplemented in
76 studies by medical records, findings on autopsy, andother sources in the ERFC. The date of the latest mortality
follow-up was Dec 31, 2015, in the ERFC and Nov 30, 2020,in the UK Biobank.

Statistical analysis
To be eligible for the analysis, recorded information about
participants’ age, sex, and history of diabetes was required.

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To focus the analysis on individuals with type 2 diabetes,
we excluded 3695 participants who were diagnosed withdiabetes at younger than 30 years and therefore would be
more likely to have type 1 diabetes. To assess dose–
response relationships, we categorised participants
according to their history of diabetes (yes or no) and theirage at diagnosis in 10-year groups: 30 years to less than
40 years, 40 years to less than 50 years, 50 years to lessthan 60 years, 60 years to less than 70 years, and 70 years
or older. We also assessed the con­tinuous shape of
associations using fractional polynomials, then calculated
adjusted associations, guided by the dose–response
analyses results and previous evidence for othercontinuous covariates. The primary outcome was allcause mortality, with additional outcomes of deaths fromcardiovascular disease, cancer, and causes other thancardiovascular disease or cancer (defined as other causes;
appendix pp 5–6). Hazard ratios (HRs) for age at diagnosis
of diabetes were calculated separately within each study
using time-dependent Cox proportional hazards
regression models (ie, allowing diabetes status, age at
diagnosis, and other covariates to change during followup, when reassessed). The timescale for the survival
analysis was duration (in years) since entry to the study atbaseline. Participants were included in analyses ofmortality outcomes irrespective of previous non-fatal
events. For each specific cause of death, participants’ data
were censored if a participant was lost to follow-up, diedfrom another cause, or reached the end of the follow-up
period. HRs calculated in this manner for each cause ofdeath are aetiologically interpretable and provide reliable
assessments of the marginal cause-specific associations—
including in the case of competing risks with low tomoderate correlations of failure times—that would be
typical of most practical circumstances.33–35 Sensitivity
analyses were conducted for cause-specific mortality
considering death from causes other than the specified
cause as competing risks using the Fine-Gray regression
model. Study-speciﬁc estimates (ie, log HRs) were thenpooled across studies by multivariate random-effects
meta-analysis, because heterogeneity was expected asa result of analysing diverse data sources.36 To avoid
model overfitting, studies with fewer than ten deaths forany outcome (ie, all-cause and cause-specific death) were
excluded from the main analyses for relevant outcomes.
Further sensitivity analyses excluded studies with fewer
than 80 deaths (ie, applying a stricter ten events pervariable rule at the study level). The proportional hazards
assumption, assessed by meta-analysis of study-specificinteraction of the coded exposure variable (indicators orcontinuous) and the survival analysis time in years, wasmet (p>0·05).
Because the principal objective of our study was toestimate reductions in life expectancy according to age at
diagnosis of diabetes, for our main analysis we calculated
HRs stratified by sex and adjusted for age only. A secondary
objective was to explore the extent to which the age-specificrelevance of diabetes could be related to other known
factors associated with mortality risk. HRs were therefore
sequentially adjusted for several variables mostly recorded
after the diagnosis of diabetes: smoking status, BMI,
systolic blood pressure, total cholesterol, measures ofglycaemia, measures of renal function, measures ofinflammation, level of education, and self-reported useof medications. These variables were selected considering
subject matter knowledge and data availability. The
order of sequential adjustment reflected prioritisation ofa variable as a confounder, mediator, or indicatorof severity of diabetes, consistent with principles of themodified disjunctive cause criterion reasoning.37 We
investigated effect modification using tests for interaction
for individual characteristics (age, sex, smoking, andhistory of cardiovascular disease) and by meta-regressionof study-specific log HRs (ie, outcome) on study-level
characteristics (diabetes diagnosis information available,
median year of baseline, and median year of followup) assuming normal error terms36 and using a 0·001
significance threshold to make some allowance formultiple testing (ie, 0·01/7 for seven interactions assessed
at 0·01 nominal significance each). Between-study
heterogeneity of log HRs was assessed by the I2 statistic.38
Details of the methods used to estimate reductions inlife expectancy by age at diagnosis of diabetes areprovided in the appendix (p 28). In brief, estimates ofcumulative survival from age 40 years onwards according
to age at diagnosis of diabetes were calculated by applying
the HRs for cause-specific mortality (specific to age at
risk and sex) to respective mortality rates obtained fromthe detailed mortality component of the US Centers
for Disease Control and Prevention’s CDC WONDER
database,39 which recorded 2·7 million deaths among
more than 320 million individuals during 2015.9,13 Thismethod does not rely on the survival estimates from thecohort data; instead, it makes inferences by estimating
age-at-risk specific HRs from the cohort data, which arethen combined with external population age-specificmortality rates.11 Supplementary analyses used EU death
rates for 2015. We calculated two-sided p values and useda significance level of p<0·05 unless stated otherwise.
Analyses were done using Stata (version 15.1).

Role of the funding source
The funders of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report.

Results
Sufficient information for inclusion was available for1 515 718 participants from 97 prospective cohorts,
comprising 1 017 695 participants from 96 ERFC cohorts
and 498 023 participants from the UK Biobank (table 1,
appendix pp 2–3). For participants from the ERFC, themedian year of recruitment was 1990 (range 1961–2007)
and the median year of latest follow-up was 2015

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463 920 (45·6%)553 775 (54·4%)

1 017 695

1 017 695

1 017 695
945 856753 256890 293594 679398 521196 159
96 802Age at baseline, years

Male sex

Female sex

Current smoker

Systolic blood pressure, mm Hg

BMI, kg/m2

Total cholesterol concentration, mmol/l

Random glucose concentration, mmol/lFasting glucose concentration, mmol/l

HbA1c, %389 924534 131

Vocational or university education level

Non-White ethnic group

17 712

Fasting glucose concentration, mmol/l

26 390 (5·3%)

298 113 (60·3%)

135 849 (27·7%)69 693 (14·0%)

4·88 (0·89)

5·15 (1·12)

5·10 (1·19)

5·70 (1·14)

52 494 (10·5%)

271 347 (54·5%)

226 676 (45·5%)

23 052 (4·9%)284 262 (60·7%)

113 681 (24·5%)

62 346 (13·2%)

4·76 (0·61)

5·04 (0·70)

4·97 (0·78)

5·76 (1·11)

27·2 (4·6)

137 (19)49 497 (10·5%)

260 999 (55·4%)

210 224 (44·6%)

56·8 (8·1)--

471 223 (94·6%)

61 154 (12·4%)

94 710 (25·9%)66 186 (11·6%)

63 734 (6·7%)

5·25 (0·48)

5·15 (0·64)

5·25 (0·87)

5·79 (1·11)

25·8 (4·1)

134 (19)252 507 (28·7%)

512 199 (54·0%)

436 576 (46·0%)

54·4 (9·8)····

948 775 (93·2%)

Participants
without diabetes

363 (23·6%)875 (58·3%)1364 (90·0%)363 (23·4%)

8·20 (2·27)

7·42 (4·23)

8·93 (4·54)

4·53 (1·10)

31·3 (7·0)

137 (17)243 (15·7%)668 (43·1%)882 (56·9%)

52·8 (8·5)

01550 (100·0%)1550 (0·3%)617 (35·0%)313 (23·4%)1060 (52·8%)422 (13·0%)

7·37 (1·89)

7·88 (3·77)

8·20 (4·04)

5·55 (1·15)

27·9 (6·0)

135 (19)914 (28·9%)2066 (63·5%)1187 (36·5%)

49·4 (8·0)

418 (12·8%)2835 (87·2%)3253 (0·3%)

30 to <40 years

1167 (20·1%)

3237 (56·7%)4707 (82·3%)1316 (22·5%)

7·37 (2·20)

7·38 (3·59)

7·92 (3·85)

4·53 (1·09)

32·1 (6·5)

138 (17)815 (14·0%)2299 (39·2%)3560 (60·8%)

54·0 (7·1)

49 (0·8%)5810 (99·2%)5859 (1·2%)

1781 (32·8%)775 (23·2%)

2610 (47·3%)1097 (12·5%)

7·14 (1·79)

7·27 (3·38)

7·98 (3·58)

5·69 (1·15)

28·6 (5·7)

138 (19)2583 (30·6%)

4613 (52·6%)4150 (47·4%)

51·0 (7·4)

3161 (36·1%)5602 (63·9%)8763 (0·9%)40 to <50 years

1234 (11·3%)

5811 (53·7%)9453 (87·1%)3122 (28·3%)

6·98 (1·76)

7·00 (2·99)

7·35 (3·07)

4·52 (1·05)

31·7 (5·7)

141 (17)1193 (10·8%)4152 (37·6%)6889 (62·4%)

60·8 (4·9)

243 (2·2%)

10 798 (97·8%)

11 041 (2·2%)3378 (30·5%)1578 (22·8%)4777 (41·5%)2256 (12·1%)

6·71 (1·59)

6·94 (2·66)

7·51 (2·96)

5·86 (1·12)

28·9 (5·5)

142 (19)4763 (26·6%)

10 359 (55·7%)8246 (44·3%)

55·5 (6·8)

11 558 (62·1%)7047 (37·9%)18 605 (1·8%)

50 to <60 years

Participants with diabetes; age at diagnosis*

522 (7·1%)3362 (46·0%)6173 (84·8%)2344 (31·7%)

6·53 (1·53)

6·23 (2·28)

6·67 (2·53)

4·63 (1·09)

30·8 (5·2)

144 (17)665 (9·0%)2898 (39·1%)4508 (60·9%)

65·4 (4·3)

588 (7·9%)6818 (92·1%)7406 (1·5%)2817 (22·1%)

1583 (21·4%)

5261 (37·8%)2820 (13·1%)

6·30 (1·22)

6·48 (2·03)

6·94 (2·57)

5·87 (1·14)

28·5 (5·2)

143 (19)4422 (21·1%)

13 731 (63·8%)7796 (36·2%)

60·6 (6·0)

15 895 (73·8%)5632 (26·2%)

21 527 (2·1%)

60 to <70 years

52 (5·5%)566 (60·1%)

471 (51·1%)

202 (21·4%)

5·66 (1·20)

5·29 (0·81)

5·66 (1·75)

5·60 (1·16)

30·3 (4·8)

145 (18)

81 (8·6%)331 (35·1%)613 (64·9%)62·0 (5·3)

939 (99·5%)

5 (0·5%)944 (0·2%)

1601 (15·3%)891 (15·8%)4162 (32·9%)2762 (16·5%)

6·07 (0·95)

5·93 (1·33)

6·42 (2·31)

5·93 (1·15)

27·8 (4·7)

146 (20)2489 (15·3%)

10 807 (64·4%)5965 (35·6%)

67·6 (4·9)

14 553 (86·8%)2219 (13·2%)

16 772 (1·6%)≥70 years

Table 1: Baseline characteristics of participants

Data are n (%) or mean (SD). Differences in characteristics across categories of age at diagnosis of diabetes were all statistically significant (p<0·001 adjusted for age and sex) based on Wald tests. ERFC=Emerging Risk Factors Collaboration. *Includes
people with a history of diabetes at the baseline survey (prevalent diabetes) and people with diabetes diagnosed during follow-up (incident diabetes). †Includes use of lipid-lowering, antihypertensive, or antidiabetic medication at baseline.

496 231426 110

Random glucose concentration, mmol/l

Non-White ethnic group465 833

494 242495 426

BMI, kg/m2

Total cholesterol concentration, mmol/l

Vocational or university education level

497 112

Systolic blood pressure, mm Hg

490 758

497 789

Current smoker

498 023

498 023

Female sex

History of cardiovascular disease

27·5 (4·8)

498 023

Medication use†

138 (19)498 023

Age at baseline, years

Male sex

462 791

1819 (0·4%)

498 023

Incident diabetes

HbA1c, %24 981 (5·0%)

498 02357·0 (8·1)

498 023 (100·0%)

498 023

Prevalent diabetes

71 348 (13·4%)99 850 (25·6%)

84 056 (13·7%)

73 091 (7·2%)

5·42 (0·79)

5·30 (1·09)

5·39 (1·23)

5·80 (1·11)

26·0 (4·3)

135 (19)267 678 (28·3%)

Number of participants

UK Biobank

614 036

Medication use†

1 017 695

45 585 (4·5%)

1 017 695

Incident diabetes

History of cardiovascular disease

23 335 (2·3%)

1 017 695
54·6 (9·7)

1 017 695 (100·0%)

1 017 695

All participants

Prevalent diabetes

Total number of participants

ERFC

N

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8·0All-cause mortality(246 596 cases)
Male
FemaleCardiovascular disease mortality(84 384 cases)

Cancer mortality(84 960 cases)

Other causes mortality(61 456 cases)

HR

4·0

2·0

1·0
No 30
diabetes

40

50

60

70

Mean age at diagnosis (years)

80

No 30
diabetes

40

50

60

70

80

Mean age at diagnosis (years)

No 30
diabetes

40

50

60

70

Mean age at diagnosis (years)

80

No 30
diabetes

40

50

60

70

80

Mean age at diagnosis (years)

Figure 1: Sex-specific HRs for all-cause and cause-specific mortality according to age at diagnosis of type 2 diabetes
The mean age at diagnosis for the categories 30 to <40 years, 40 to <50 years, 50 to <60 years, 60 to <70 years and ≥70 years is plotted on the x axis. HRs are adjusted
for age, and the reference (1·0) is people without diabetes. Studies with fewer than ten events of any outcome were excluded from the analysis of that outcome.
The sizes of the boxes are proportional to the inverse of the variance of the log-transformed HRs. Vertical lines represent 95% CIs. HR=hazard ratio.

(range 1980–2013); the corresponding values for the UK
Biobank were 2009 and 2020. In the ERFC, mostparticipants were enrolled in Europe (52·0%) or the USA
and Canada (39·8%). Overall, the mean age of participants
at baseline was 55·0 years (SD 9·2); 690 596 participants(45·6%) were male and 825 122 (54·4%) were female. In
the ERFC, the age at diagnosis was available for 23 335(56·7%) of 41 160 participants who had prevalent diabetes
at baseline, and a further 45 585 participants werediagnosed with diabetes during follow-up (ie, new-onset
disease). In the UK Biobank, the age at diagnosis wasavailable for 24 981 (98·3%) of 25 416 participants who
had prevalent diabetes, and a further 1819 participants
were diagnosed with diabetes during follow-up. The
mean age at diagnosis was 54·1 years (SD 9·0) for
participants with prevalent diabetes and 64·9 years (8·5)
for those with incident diabetes. Over a median follow-up
of 12·5 years (5 – 95th percentiles 5·0–32·1; 23·1 million
person-years at risk), 246 670 deaths were recorded, of
which 84 443 were due to cardiovascular causes, 150 972
due to non-cardiovascular causes, and 11 255 due tounknown or ill-defined causes. The non-cardiovascularcauses could be further categorised into 85 014 due
to cancer and 61 516 due to causes other thancardiovascular disease or cancer (hereafter termed othercauses; mainly diseases of the respiratory system or
nervous system, infections, and external causes); theremaining 4442 deaths in this category had been coded as
non-cardiovascular causes without the possilibilty offurther subdivision (appendix p 5).
In analyses adjusted for age, we observed a linear dose–
response association between earlier age at diag­nosis of
diabetes and higher risk of all-cause mortality, mortality
due to cardiovascular disease, and mortality from othercauses for each sex (figure 1). Findings were broadly
similar in combined analyses adjusted for sex andcontinuous modelling with fractional polynomials(appendix p 13). In further adjusted analyses, we used datafrom 92 cohorts and 1 132 277 participants with complete
information on age at diagnosis of diabetes, age, sex,
smoking status, BMI, systolic blood pressure, and totalcholesterol. Compared with participants without a historyof diabetes, HRs for all-cause mortality, adjusted for age
and sex only, were 2·69 (95% CI 2·43–2·97) for thosediagnosed at age 30–39 years, 2·26 (2·08–2·45) atage 40–49 years, 1·84 (1·72–1·97) at age 50–59 years,
1·57 (1·47–1·67) at age 60–69 years, and 1·39 (1·29–1·51)
for those diagnosed aged 70 years and older (table 2). For
participants diagnosed with diabetes aged 30–39 years,
HRs were 4·20 (3·57–4·94) for vascular mortality,
1·55 (1·30–1·85) for cancer mortality, and 3·99 (3·50–4·55)
for mortality from other causes. Across all ages, HRs
per decade of earlier diagnosis of diabetes were
1·14 (1·08–1·19) for all-cause mortality, 1·19 (1·11–1·27)
for vascular mortality, 0·95 (0·88–1·02) for cancer
mortality, and 1·18 (1·10–1·27) for mortality from othercauses (table 2).
HRs for all-cause mortality changed little after
additional adjustment for other risk factors (BMI, systolicblood pressure, and total cholesterol; table 2). However,HRs were attenuated substantially after further
adjustment for measures of glycaemia (ie, fasting glucose
or HbA1c), a pattern also observed for cause-specificmortality (appendix p 7). HRs showed little change after
adjustment for measures of renal function (ie, estimatedglomerular filtration rate), inflammation (ie, C-reactive
protein), or lipids (ie, non-HDL cholesterol, HDL
cholesterol, and triglycerides; appendix p 7).
Broadly similar HRs to those reported in the previous
paragraphs were observed in sensitivity analyses thatcompared results by study-level information available ondiabetes (prevalent disease, incident disease, or both) andby participant characteristics (age, sex, smoking status,and history of cardiovascular disease; appendix p 14). HRs

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Numberof events

Adjusted HR (95% CI)
Age and sex

Age, sex, andsmokingAge, sex, smoking,and other risk
factors*

All-cause mortality
Participants without diabetes

153 068

1 (ref)1 (ref)1 (ref)Diagnosed aged 30 to <40 years

676

2·69 (2·43–2·97)

2·74 (2·49–3·02)

2·64 (2·41–2·90)

Diagnosed aged 40 to <50 years

2070

2·26 (2·08–2·45)

2·33 (2·14–2·53)

2·24 (2·06–2·43)

Diagnosed aged 50 to <60 years

4197

1·84 (1·72–1·97)

1·87 (1·75–1·99)

1·79 (1·69–1·90)

Diagnosed aged 60 to <70 years

4125

1·57 (1·47–1·67)

1·60 (1·51–1·70)

1·55 (1·46–1·64)

Diagnosed aged ≥70 years

3026

1·39 (1·29–1·51)

1·43 (1·33–1·55)

1·41 (1·31–1·53)

167 1621·14 (1·08–1·19)

1·13 (1·08–1·19)

1·13 (1·07–1·19)

Per decade earlier
p value†··<0·0001<0·0001<0·0001I2 (95% CI)··

67 (59–74)

68 (60–74)

67 (60–74)

Cardiovascular disease mortality
Participants without diabetes

53 857

Diagnosed aged 30 to <40 years278

1 (ref)1 (ref)1 (ref)

4·20 (3·57–4·94)

4·26 (3·65–4·99)

3·93 (3·41–4·53)

Diagnosed aged 40 to <50 years

821

3·19 (2·80–3·64)

3·31 (2·90–3·76)

2·93 (2·60–3·30)

Diagnosed aged 50 to <60 years

1564

2·31 (2·10–2·53)

2·36 (2·16–2·58)

2·10 (1·94–2·27)

Diagnosed aged 60 to <70 years

1580

1·95 (1·78–2·14)

1·98 (1·82–2·17)

1·81 (1·67–1·97)

Diagnosed aged ≥70 years

1251

1·50 (1·35–1·67)

1·54 (1·38–1·71)

1·48 (1·33–1·65)

Per decade earlier

59 351

1·19 (1·11–1·27)

1·19 (1·11–1·27)

1·19 (1·11–1·28)

p value†··<0·0001<0·0001<0·0001I2 (95% CI)··

58 (47–67)

59 (48–67)

59 (48–67)

Cancer mortality
Participants without diabetes

53 2171 (ref)1 (ref)1 (ref)Diagnosed aged 30 to <40 years

124

1·55 (1·30–1·85)

1·56 (1·31–1·86)

1·48 (1·24–1·76)

Diagnosed aged 40 to <50 years

433

1·28 (1·16–1·42)

1·32 (1·19–1·45)

1·25 (1·13–1·37)

Diagnosed aged 50 to <60 years

1211

1·33 (1·22–1·46)

1·37 (1·25–1·49)

1·35 (1·26–1·44)

Diagnosed aged 60 to <70 years

1178

1·27 (1·17–1·38)

1·29 (1·19–1·40)

1·28 (1·19–1·37)

Diagnosed aged ≥70 years

593

1·29 (1·18–1·42)

1·32 (1·20–1·44)

1·31 (1·20–1·42)

56 7560·95 (0·88–1·02)

0·95 (0·88–1·02)

0·94 (0·88–1·02)

Per decade earlier
p value†

··

0·18

0·16

0·13

I2 (95% CI)··

26 (2–44)

24 (0–43)

24 (0–43)

Other causes mortality
Participants without diabetes

35 986

Diagnosed aged 30 to <40 years

250

1 (ref)1 (ref)1 (ref)

3·99 (3·50–4·55)

4·04 (3·54–4·60)

3·90 (3·42–4·45)

Diagnosed aged 40 to <50 years

701

3·24 (2·88–3·64)

3·34 (2·96–3·76)

3·31 (2·95–3·71)

Diagnosed aged 50 to <60 years

1224

2·31 (2·09–2·54)

2·37 (2·15–2·60)

2·38 (2·16–2·64)

Diagnosed aged 60 to <70 years

1137

1·84 (1·67–2·02)

1·87 (1·70–2·05)

1·88 (1·71–2·06)

Diagnosed aged ≥70 years

861

1·66 (1·48–1·87)

1·71 (1·52–1·93)

1·76 (1·56–1·98)

40 1591·18 (1·10–1·27)

1·18 (1·09–1·27)

1·16 (1·08–1·25)

Per decade earlier
p value†··<0·0001<0·0001<0·0001I2 (95% CI)··

50 (35–61)

51 (36–62)

50 (36–62)

Analyses based on data from the Emerging Risk Factors Collaboration and the UK Biobank, including 92 cohorts and
1 132 277 participants with complete information on age at diagnosis of diabetes, age, sex, smoking, and other risk
factors. HR=hazard ratio. *Other risk factors were BMI, systolic blood pressure, and total cholesterol. †p value for linear
analyses per decade earlier.

Table 2: Adjusted HRs for all-cause and cause-specific mortality according to age at diagnosis of type 2
diabetes

differed slightly according to the study-level median yearof the study enrolment or follow-up period (appendix
pp 14–15) and by data source (ie, ERFC or UK Biobank;6

appendix p 16). Tests for interactions on an additive
scale were generally supportive of positive interactionsof female sex, current smoking, older age, and history of
cardiovascular disease with diabetes status categorised
according to age at diagnosis (appendix p 8). Associations
were also broadly similar in analyses that estimated HRs
for all-cause and cause-specific mortality according toduration of diabetes (ie, time since diagnosis) rather thanage at diagnosis (appendix pp 9–10, 17). Supplementary
analyses of mortality from causes other than cardio­
vascular disease suggested broadly similar associations
for cancer mortality components but notable variations in
the magnitude of associations for components ofmortality from other causes (appendix p 11)—eg, HRs per
decade earlier diagnosis of diabetes were 1·46 (95% CI
1·16–1·84) for renal disease mortality, 1·28 (1·07–1·53)
for infection-related mortality, 1·21 (1·04–1·42) for
external causes of mortality, 1·20 (1·03–1·40) for digestivesystem disease mortality, and 1·07 (0·96–1·19) for
respiratory system disease mortality. Results of causespecific mortality were broadly similar when using
competing risks-adjusted analyses (appendix p 12). Lossto follow-up was less than 10% in most studies, but the
proportion of right-censored participants and causespecific deaths varied between cohorts (appendix
pp 18–19). Sensitivity analyses that excluded studies withfewer than 80 cause-specific deaths showed similarfindings to the main analyses that excluded studies withfewer than ten deaths for any outcome (appendix p 12).
Compared with the absence of diabetes at different
attained ages, earlier age at diagnosis of diabetes was
associated with greater reductions in life expectancy,
using 2015 death rates from the USA (figure 2). For
example, at age 50 years, individuals who were diagnosed
with diabetes aged 30 years died, on average, about
14 years earlier than individuals without diabetes; thosediagnosed aged 40 years died around 10 years earlier, andthose diagnosed aged 50 years died around 6 years earlier(figure 2). These estimates were slightly higher in women(16, 11, and 7 years) than in men (14, 9, and 5 years;
figure 2). Depending on age and sex, deaths due tocardiovascular disease accounted for about 30–45% of the
reduction in life expectancy associated with diabetes, withthe remaining proportion being largely due to deaths
from causes other than cardiovascular disease or cancer(appendix p 20). Findings were broadly similar in analyses
using EU death rates from 2015, with corresponding
estimates of death at around 13, 9, or 5 years earlier
on average (appendix p 21). In supplementary analyses
that included people diagnosed with diabetes before
the age of 30 years, we found similar patterns inestimated reductions in life expectancy; the highest
estimated reductions were in people diagnosed in theyoungest age groups and were notably higher in womenthan in men (appendix pp 22–23). At age 50 years, theestimates corresponded to about 2–3 years reduction per
decade of earlier diagnosis.

www.thelancet.com/diabetes-endocrinology Published online September 11, 2023 https://doi.org/10.1016/S2213-8587(23)00223-1

Articles

Discussion

Male, all causes
18

Age at diagnosis
of diabetes (years)
30
40
50
60
70

16

Years of life lost vs no diabetes

14
12
10
8
6
4
2
0
Female, all causes
18
16
14
Years of life lost vs no diabetes

By analysing more than 23 million person-years oflongitudinal data from population cohorts in 19 highincome countries, we found a steep linear dose–response
association between earlier age at diagnosis of diabetes
and higher risk of all-cause mortality. Overall, every decadeof earlier diagnosis of diabetes was associated with about
3–4 years of reduced life expectancy. Our modelling has
suggested that, for individuals surviving to age 50 years,
those diagnosed with diabetes aged 30 years died
14 years earlier than individuals without diabetes, thosediagnosed aged 40 years died 10 years earlier, andthose diagnosed aged 50 years died 6 years earlier. The
strongest associations with earlier age at diagnosis of
diabetes were for vascular (eg, myocardial infarction andstroke) and other non-neoplastic causes of death—mainly
respiratory, neuro­
logical, and infectious diseases andexternal causes. Our estimates show that the reduction inlife expectancy associated with diabetes is slightly greater
for women than for men. These findings suggest thathigh priority should be given to developing and imple­
menting interventions that prevent or delay onset of
diabetes, especially as the prevalence of diabetes among
younger adults is increasing globally.3
Our observation of higher HRs for mortality withearlier age at diagnosis of diabetes suggests that therelative effect of diabetes is greatest at ages atwhich the underlying risk of mortality in the general
population is lowest. Such effects have previous been
observed for other cardiovascular risk factors, including
blood pressure40 and LDL-cholesterol.41 Conversely, inolder adults, in whom the underlying mortality risk ishigh, the proportional relevance of diabetes is smaller.
Previous studies have suggested that individuals whodevelop type 2 diabetes at younger ages might havemore aggressive phenotypes42 (characterised by higher
BMI, higher blood pressure, higher concentrations ofproatherogenic lipids,43 and faster deterioration in gly­
caemic control24,44) than individuals who develop diabetes
at older ages, potentially leading to premature mortality.45
Our findings are consistent with this hypothesis,suggesting that the large excess mortality associated withdiabetes at younger ages could, in part, reflect cumulative
exposure to worsened metabolic profiles. Furthermore,
we observed substantial atten­uation of excess mortality
associated with diabetes after adjustment for glycaemic
markers, suggesting that early detection of diabetes byscreening and intensive glucose management are relevantto the prevention of long-term complications in adults
with type 2 diabetes.46–48
Our study had several strengths and is distinctive andcomplimentary to previous studies.7–19,26–29 Our focus onage at diagnosis of diabetes avoided the inherent
difficulties in defining age at onset of diabetes (which
could require near continuous assessment of glycaemic
status)49 and in defining the duration of diabetes (which
could be confounded by the timing of entry into, and

12
10
8
6
4
2
0

40

45

50

55

60 65 7075
Age at risk (years)

80

85

90

95

Figure 2: Estimated years of life lost by age at diagnosis of type 2 diabetes
compared with people without diabetes
Estimates of cumulative survival from age 40 years onwards according to age at
diagnosis of diabetes, calculated by applying hazard ratios (specific to age at risk)
for all-cause mortality associated with age at diagnosis to 2015 US death rates atage 40 years or older.

the duration of participation in, prospective cohort
studies). Furthermore, our study estimated age at
diagnosis of diabetes using information from people
diagnosed with prevalent diabetes and those diagnosed
with incident diabetes. Our access to individualparticipant data avoided the limitations of previous
literature-based reviews, allowing extensive sensitivity
analyses to assess potential sources of heterogeneityand interactions according to study-level and individuallevel characteristics. Our estimation of reductions inlife expectancy relied on age-specific HRs directly
estimated from individual-level data and applied tocontemporary population-specific mortality rates. Thisapproach was desirable because HRs are often lessvariable across similar populations and time and can be
more precisely estimated in combined data synthesis as
in our study. The generalisability of the findings waswww.thelancet.com/diabetes-endocrinology Published online September 11, 2023 https://doi.org/10.1016/S2213-8587(23)00223-1

7

Articles

enhanced by inclusion of data from 97 prospective
studies from 19 high-income countries recruited
between 1961 and 2009, with latest follow-up between
1980 and 2020.
Our study also had potential limitations. The con­tributing prospective studies defined diabetes in varying
ways; however, we found no major differences in results
across studies due to such variation. Between-study
heterogeneity of associations was moderate to high, andwas not explained by the characteristics assessed insubgroup analyses. We did not have information on thepathophysiological subtype of diabetes; however, giventhat we excluded participants who were diagnosed withdiabetes at younger than 30 years, inferring that the large
majority of participants had type 2 diabetes could bereasonable.50 We did not have information on whether
individuals with diabetes were treated or followed-up
differently depending on their age at diagnosis or
duration of diabetes (eg, in terms of type of medication,dose or intensity of treatment)—such factors would be
likely to affect long-term disease outcomes. Residual
confounding due to measurement error in variablesconsidered for adjustment (eg, smoking) has not beenaddressed. We also did not have information on other comorbidities (eg, mental health conditions) andsocioeconomic variables that would have been useful toadjust for. Our analysis involved participants who were
mostly of European continental ancestry; future studies
should seek to evaluate these results in other ethnic andracial groups. Finally, although we found broadly similar
results for cause-specific mortality using competingand non-competing adjusted models, the aetiologicalinterpretation is limited for models adjusted for com­
peting risk.51 However, non-competing risk-adjusted
models might be subject to selection bias, because HRs
are calculated conditional on those who have survived.52
In conclusion, this study suggests that every decadeof earlier diagnosis of diabetes is associated with about
3–4 years of lower life expectancy, highlighting the needto develop and implement interventions that prevent ordelay the onset of diabetes and to intensify the treatmentof risk factors among young adults diagnosed withdiabetes.
Emerging Risk Factors Collaboration
Writing committee: Stephen Kaptoge, Luanluan Sun, Matthew Walker,
Sarah Spackman, Lisa Pennells (BHF Cardiovascular Epidemiology
Unit, Department of Public Health and Primary Care, University ofCambridge, Cambridge, UK and Victor Phillip Dahdaleh Heart and Lung
Research Institute, University of Cambridge, Cambridge, UK),
Sreenivasa Rao Kondapally Seshasai (Cardiovascular Clinical Academic
Group, Molecular and Clinical Sciences Research Institute, St. George’s
University of London, London, UK), Thomas Bolton(BHF Cardiovascular Epidemiology Unit, Department of Public Healthand Primary Care, University of Cambridge, Cambridge, UK and
BHF Data Science Centre, Health Data Research UK, London, UK),
Feven Ataklte (Department of Internal Medicine, Boston Medical Centerand Boston University School of Medicine, Boston, MA, USA), Peter
Willeit (BHF Cardiovascular Epidemiology Unit, Department of Public
Health and Primary Care, University of Cambridge, Cambridge, UK andClinical Epidemiology Team, Medical University of Innsbruck,

8

Innsbruck, Austria), Steven Bell (BHF Cardiovascular Epidemiology
Unit, Department of Public Health and Primary Care, University ofCambridge, Cambridge, UK and Department of Clinical Neurosciences,
University of Cambridge, Cambridge, UK), Steven Burgess(BHF Cardiovascular Epidemiology Unit, Department of Public Healthand Primary Care, University of Cambridge, Cambridge, UK,
BHF Centre of Research Excellence, School of Clinical Medicine,
Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK, andMedical Research Council Biostatistics Unit, University of Cambridge,
Cambridge, UK), Servet Altay (Department of Cardiology, Trakya
University School of Medicine, Erdine, Turkey), Gerd Assmann(Assmann Foundation for Prevention, Münster, Germany),
Yoav Ben-Shlomo (Population Health Sciences, University of Bristol,
Bristol, UK), Lyle G Best (Missouri Breaks Industries Research, Eagle
Butte, SD, USA), Cecilia Björkelund (Institute of Medicine, Sahlgrenska
Academy, University of Gothenburg, Gothenburg, Sweden),
Dan G Blazer (School of Medicine, Duke University, Durham, NC,
USA), Hermann Brenner (Division of Clinical Epidemiology and Aging
Research, German Cancer Research Center, Heidelberg, Germany and
Network Aging Research, Heidelberg University, Heidelberg, Germany),
Eric J Brunner (Department of Epidemiology and Public Health,
University College London, London, UK), Gilles R Dagenais(Quebec Heart and Lung Institute, Quebec, QC, Canada),
Jackie A Cooper (William Harvey Research Institute, NIHR Barts
Biomedical Research Centre, Queen Mary University of London,
London, UK), Cyrus Cooper (MRC Lifecourse Epidemiology Centre
University of Southampton, Southampton, UK), Carlos J Crespo(Oregon Health and Science University and Portland State University
Joint School of Public Health, Portland, OR, USA), Mary Cushman(Larner College of Medicine, The University of Vermont, Burlington,
VT, USA), Ralph B D’Agostino Sr (Mathematics and Statistics
Department, Boston University, Boston, MA, USA), Makoto Daimon(Global Center of Excellence Program Study Group, Yamagata University
Faculty of Medicine, Yamagata, Japan and Department of Endocrinology
and Metabolism, Hirosaki University Graduate School of Medicine,
Aomori, Japan), Lori B Daniels (UCSD Division of Cardiovascular
Medicine, Sulpizio Cardiovascular Center, La Jolla, CA, USA),
Rachel Dankner (The Gertner Institute for Epidemiology and Health
Policy Research, Sheba Medical Center, Tel Hashomer, Israel; School of
Public Health, Department of Epidemiology and Preventive Medicine,
Tel Aviv University, Tel Aviv, Israel; and The Feinstein Institute for
Medical Research, Northwell Health, Manhasset, NY, USA),
Karina W Davidson (The Feinstein Institute for Medical Research,
Northwell Health, Manhasset, NY, USA), Renate T de Jongh(Amsterdam University Medical Centers, VUMC, Amsterdam,
Netherlands), Chiara Donfrancesco (Department of Cardiovascular,
Endocrine-metabolic Diseases and Aging, Istituto Superiore di Sanità,Rome, Italy), Pierre Ducimetiere (Inserm - Université Paris Sud CESP Villejuif, Paris, France), Petra J M Elders (Department of General
Practice, Amsterdam UMC, Vrije Universiteit, Amsterdam Public
Health Research Institute, Amsterdam, Netherlands), Gunnar Engström(Department of Clinical Sciences Malmö, Lund University, Malmö,
Sweden), Ian Ford (Institute of Health and Wellbeing, University ofGlasgow, Glasgow, UK), John Gallacher (Department of Psychiatry,
University of Oxford, Oxford, UK), Stephan J L Bakker (Department
of Internal Medicine, University Medical Centre Groningen, Universityof Groningen, Groningen, Netherlands), Uri Goldbourt (Department of
Epidemiology and Preventive Medicine, School of Public Health,
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel),
Agustin Gómez de la Cámara (12 Octubre Hospital Research Institute,
Madrid, Spain), Sameline Grimsgaard (Department of Community
Medicine, UiT The Arctic University of Norway, Tromsø, Norway),
Vilmundur Gudnason (Faculty of Medicine, University of Iceland,
Reykjavik, Iceland and Icelandic Heart Association, Kopavogur, Iceland),
Per-Olof Hansson (Region Västra Götaland, Sahlgrenska University
Hospital, Department of Medicine Geriatrics and Emergency Medicine/Östra, Gothenburg, Sweden and Institute of Medicine, Department of
Molecular and Clinical Medicine, Sahlgrenska Academy, Gothenburg
University, Gothenburg, Sweden), Hironori Imano (Public Health,Osaka University Graduate School of Medicine, Suita, Japan),
J Wouter Jukema (Department of Cardiology, Leiden University Medical

www.thelancet.com/diabetes-endocrinology Published online September 11, 2023 https://doi.org/10.1016/S2213-8587(23)00223-1

Articles

Center, Netherlands; Einthoven Laboratory for Experimental Vascular
Medicine, LUMC, Leiden, Netherlands; and Netherlands Heart Institute,
Utrecht, Netherlands), Christopher Kabrhel (Center for Vascular
Emergencies, Department of Emergency Medicine, Massachusetts
General Hospital, Harvard Medical School, Boston, MA, USA),
Jussi Kauhanen (University of Eastern Finland, Kuopio, Finland),Maryam Kavousi (Department of Epidemiology, Erasmus MC,
University Medical Center Rotterdam, Rotterdam, Netherlands),
Stefan Kiechl (Department of Neurology, Medical University Innsbruck,
Innsbruck, Austria), Matthew W Knuiman (School of Population and
Global Health, The University of Western Australia, Crawley, WA,
Australia), Daan Kromhout (Department of Epidemiology, University ofGroningen, University Medical Center Groningen, Groningen,
Netherlands), Harlan M Krumholz (Center for Outcomes Research and
Evaluation, Yale-New Haven Hospital, New Haven, CT, USA;
Department of Health Policy and Management, Yale School of Public
Health, New Haven, CT, USA; and Section of Cardiovascular Medicine,
Department of Internal Medicine, Yale School of Medicine, New Haven,
CT, USA), Lewis H Kuller* (Department of Epidemiology, University ofPittsburgh, Pittsburgh, PA, USA), Tiina Laatikainen (National Institutefor Health and Welfare, Helsinki, Finland), Debbie A Lawlor(MRC Integrative Epidemiology Unit at the University of Bristol, Bristol,
UK and Population Health Science, Bristol Medical School, University ofBristol, UK), Haakon E Meyer (Norwegian Institute of Public Health,Oslo, Norway), Kenneth Mukamal (Beth Israel Deaconess Medical
Centre, Harvard Medical School, Harvard University, Boston, MA, USA),Paul J Nietert (Department of Public Health Sciences, Medical
University of South Carolina, Charleston, SC, USA),
Toshiharu Ninomiya (Department of Epidemiology and Public Health,Graduate School of Medical Sciences, Kyushu University, Fukuoka,
Japan), Dorothea Nitsch (Department of Non-communicable Disease
Epidemiology, London School of Hygiene and Tropical Medicine,
London, UK), Børge G Nordestgaard (Department of Clinical
Biochemistry and the Copenhagen General Population Study, Herlev
and Gentofte Hospital, Copenhagen University Hospital, Denmark;The Copenhagen City Heart Study, Frederiksberg Hospital, Copenhagen
University Hospital, Denmark; and Department of Clinical Medicine,
Faculty of Health and Medical Sciences, University of Copenhagen,Denmark), Luigi Palmieri (Department of Cardiovascular, Dysmetabolicand Ageing-Associated Diseases, Istituto Superiore di Sanità, Rome,
Italy), Jackie F Price (Usher Institute, University of Edinburgh,
Edinburgh, UK), Paul M Ridker, Qi Sun (Center Cardiovascular Disease
Prevention, Division of Preventive Medicine and Division of
Cardiovascular Diseases, Brigham and Women’s Hospital, Harvard
Medical School, Harvard University, Boston, MA, USA),
Annika Rosengren (Sahlgrenska University Hospital, Gothenburg,
Sweden and Östra Hospital, Gothenburg, Sweden), Ronan Roussel*(Chef de Service Endocrinologie Diabétologie Nutrition, Département
Hospitalo-Universitaire FIRE Groupe Hospitalier Bichat Claude Bernard, Paris, France), Masaru Sakurai (Department of Socialand Environmental Medicine, Kanazawa Medical University, Uchinada,
Japan), Veikko Salomaa (National Institute for Health and Welfare,Helsinki, Finland), Ben Schöttker (Division of Clinical Epidemiology
and Aging Research, German Cancer Research Center (DKFZ),
Heidelberg, Germany and Network Aging Research, University of
Heidelberg, Heidelberg, Germany), Jonathan E Shaw (Clinical Diabetes
and Epidemiology, Baker Heart and Diabetes Institute, Melbourne, VIC,
Australia), Timo E Strandberg (University of Helsinki and Helsinki
University Hospital, Helsinki, Finland and University of Oulu,
Center for Life Course Health Research, Oulu, Finland),
Johan Sundström (Department of Medical Sciences, Uppsala University,
Uppsala, Sweden), Hanna Tolonen (Department of Public Health
Solutions, National Institute for Health and Welfare (THL), Helsinki,
Finland), Aage Tverdal (Norwegian Institute of Public Health, Centre for
Fertility and Health, Oslo, Norway), WM Monique Verschuren (National
Institute for Public Health and the Environment (RIVM), Bilthoven,
Netherlands and Julius Center for Health Sciences and Primary Care,
University Medical Center Utrecht, Utrecht University, Utrecht,
Netherlands), Henry Völzke (Universitätsmedizin Greifswald, Institut
für Community Medicine, Abteilung SHIP/ Klinisch-Epidemiologische
Forschung, Greifswald, Germany), Lynne Wagenknecht (Wake Forest

School of Medicine, Wake Forest University, Winston-Salem, NC, USA),
Robert B Wallace (College of Public Health, University of Iowa, Iowa, IA,
USA), S Goya Wannamethee (Department of Primary Care andPopulation Health, University College London, London, UK),
Nicholas J Wareham (MRC Epidemiology Unit, School of Clinical
Medicine, University of Cambridge, Cambridge, UK),
Sylvia Wassertheil-Smoller (Department of Epidemiology and Population
Health, Albert Einstein College of Medicine, New York, NY, USA),
Kazumasa Yamagishi (Department of Public Health Medicine, Faculty of
Medicine and Health Services Research and Development Center,
University of Tsukuba, Tsukuba, Japan ), Bu B Yeap (Medical School,
The University of Western Australia, Perth, WA, Australia andDepartment of Endocrinology and Diabetes, Fiona Stanley Hospital,
Perth, WA, Australia), Seamus Harrison, Simon G Thompson(BHF Cardiovascular Epidemiology Unit, Department of Public Healthand Primary Care, University of Cambridge, Cambridge, UK),
Michael Inouye (BHF Cardiovascular Epidemiology Unit, Department of
Public Health and Primary Care, University of Cambridge, Cambridge,
UK; Victor Phillip Dahdaleh Heart and Lung Research Institute,
University of Cambridge, Cambridge, UK; BHF Centre of Research
Excellence, School of Clinical Medicine, Addenbrooke’s Hospital,
University of Cambridge, Cambridge, UK; Baker Heart and Diabetes
Institute, Melbourne, VIC, Australia; Health Data Research UK
Cambridge, Wellcome Genome Campus and University of Cambridge,
Hinxton, UK; Cambridge Baker Systems Genomics Initiative,
Department of Public Health and Primary Care, University ofCambridge, Cambridge, UK; and Department of Clinical Pathology,
University of Melbourne, Parkville, VIC, Australia), Simon Griffin (MRC
Epidemiology Unit, School of Clinical Medicine, University ofCambridge, Cambridge, UK and Primary Care Unit, Department of
Public Health and Primary Care, University of Cambridge, Cambridge,UK), Adam S Butterworth (BHF Cardiovascular Epidemiology Unit,
Department of Public Health and Primary Care, University ofCambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung
Research Institute, University of Cambridge, Cambridge, UK; BHF
Centre of Research Excellence, School of Clinical Medicine,
Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK;
Health Data Research UK Cambridge, Wellcome Genome Campus andUniversity of Cambridge, Hinxton, UK; and NIHR Blood and TransplantResearch Unit in Donor Health and Behaviour, University of Cambridge,
Cambridge, UK), Angela M Wood (BHF Cardiovascular Epidemiology
Unit, Department of Public Health and Primary Care, University ofCambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung
Research Institute, University of Cambridge, Cambridge, UK;
BHF Centre of Research Excellence, School of Clinical Medicine,
Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK;
Health Data Research UK Cambridge, Wellcome Genome Campus andUniversity of Cambridge, Hinxton, UK; NIHR Blood and TransplantResearch Unit in Donor Health and Behaviour, University of Cambridge,
Cambridge, UK; and Cambridge Centre for AI in Medicine, Cambridge,UK), Naveed Sattar† (Institute of Cardiovascular and Medical Sciences,
University of Glasgow, Glasgow, UK), John Danesh† (BHF
Cardiovascular Epidemiology Unit, Department of Public Health andPrimary Care, University of Cambridge, Cambridge, UK; Victor Phillip
Dahdaleh Heart and Lung Research Institute, University of Cambridge,
Cambridge, UK; BHF Centre of Research Excellence, School of Clinical
Medicine, Addenbrooke’s Hospital, University of Cambridge,
Cambridge, UK; NIHR Blood and Transplant Research Unit in Donor
Health and Behaviour, University of Cambridge, Cambridge, UK; Health
Data Research UK Cambridge, Wellcome Genome Campus andUniversity of Cambridge, Hinxton, UK; and Department of
Human Genetics, Wellcome Sanger Institute, Hinxton, UK),
Emanuele Di Angelantonio† (BHF Cardiovascular Epidemiology Unit,
Department of Public Health and Primary Care, University ofCambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung
Research Institute, University of Cambridge, Cambridge, UK;
BHF Centre of Research Excellence, School of Clinical Medicine,
Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK;
Health Data Research UK Cambridge, Wellcome Genome Campus andUniversity of Cambridge, Hinxton, UK; NIHR Blood and TransplantResearch Unit in Donor Health and Behaviour, University of Cambridge,

www.thelancet.com/diabetes-endocrinology Published online September 11, 2023 https://doi.org/10.1016/S2213-8587(23)00223-1

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Cambridge, UK; and Health Data Science Centre, Human Technopole,
Milan, Italy). *Deceased †Joint senior authors
Contributors
SK, NS, JD, and EDA conceived and designed the study; all authorsacquired and interpreted the data; SK and EDA performed the analyses
and have access to all data in the study; SK, EDA, LS, NS, andJD drafted the manuscript, which was critically revised by all authors.
SK and EDA verified the underlying data. All authors are responsible
for the decision to submit the manuscript for publication.
Declaration of interests
ASB reports grants outside of this work from AstraZeneca, Bayer,
Biogen, BioMarin, Merck, Novartis, and Sanofi. BGN reports
consulting fees form AstraZeneca, Sanofi, Regeneron Pharmaceuticals,
Ionis Pharmaceuticals, Amgen, Kowa Pharmaceuticals, Denka,
Amarin, Novartis, Novo Nordisk, Esperion Therapeutics, Silence
Therapeutics, and Ultragenyx; payment or honoraria for lectures,presentations, speakers bureaus, manuscript writing, or educational
events from Sanofi, Amgen, Kowa Pharmaceuticals, Denka, Amarin,
Novartis, Novo Nordisk, Abbott Laboratories, Mankind Pharma; andparticipation on a data safety monitoring board or advisory board forAstraZeneca, Ionis Pharmaceuticals, Kowa Pharmaceuticals, Novartis,
and Esperion. BBY reports grants from the National Health andMedical Research Council paid to his institutions (Medical School,
The University of Western Australia, Perth, WA, Australia; andDepartment of Endocrinology and Diabetes, Fiona Stanley Hospital,
Perth, WA, Australia). CK report grants from the US National Institutesof Health (NIH); grants or contracts from Grifols and Diagnostica
Stago; consulting fees from BMS Pfizer; participation on a data safety
monitoring board or advisory board for BMS Pfizer; a leadership or
fiduciary role on the RECOVER-CDC Steering Committee; and stock orstock options for Insera. CC reports consulting fees from the Alliancefor Better Bone Health, Amgen, Eli Lilly, GSK, Medtronic, Merck,
Novartis, Pfizer, Roche, Servier, Takeda, and UCB. DAL reports grants
from the UK Medical Research Council (MRC), National Institute for
Health and Care Research (NIHR), BHF, European Research Council,
NIH, and Diabetes UK paid to her institutions (MRC Integrative
Epidemiology Unit and Population Health Science, Bristol Medical
School, University of Bristol, Bristol, UK); and participation on a data
safety monitoring board or advisory board for the UK Biobank,Bradford Institute Health Research, and NIHR-BHF. DN reports
grants outside of this work from GSK and participation on a data safety
monitoring board or advisory board for GSK. EDA reports grants fromthe BHF, NIHR, and an NIHR Senior Investigator Award; andparticipation on a data safety monitoring board or advisory board from
Our Future Health and EURAC Research. HMK reports grants outsideof this work from the NIH Agency for Healthcare Research andQuality, Foundation for a Smoke-Free World, State of CT Departmentof Public Health, US Food and Drug Administration, Johnson& Johnson, American Heart Association, Centers for Medicare &Medicaid Services, Google, and Pfizer; consultant fees from
Massachusetts Medical Society, Eyedentifeye, and F-Prime;
participation on a data safety monitoring board or advisory board for
Aetna, Reality Labs, Element Science, and United Health; stock orstock options for Element Science and Eyedentifeye; and is a cofounder of Hugo Health and Refactor Health. JSu reports stock or stock
options from Anagram Kommunikation, and Symptoms Europe.
JD reports support from a BHF Professorship and NIHR Senior
Investigator Award; and grants or contracts from Merck Sharp &Dohme, Novartis, Pfizer, and AstraZeneca, outside the submitted work.
JG report grants or contracts from the MRC; payment or honoraria for
lectures and support for attending meetings and/or travel from Yonsei
University; and leadership or a fiduciary role for Dementias Platform
UK and the BrainWaves study. JSh reports grants or contracts from
AstraZeneca; consulting fees from AstraZeneca, Sanofi, Novo Nordisk,
MSD, Eli Lilly, and Pfizer; and payment or honoraria for lectures andsupport for attending meetings and/or travel from Astra Zeneca,
Mylan, Sanofi, Boehringer Ingelheim, Zuellig Pharma, and Abbott.
KJM reports grants from the NIH. LS is now a full-time employee atRegeneron Genetics Center. LEW reports grants from the National
Heart, Lung, and Blood Institute (NHLBI), NIH. NS reports grants

10

paid to their institution (University of Glasgow, Glasgow, UK) from
AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics;
consulting fees from Abbott Laboratories, Afimmune, Amgen,
AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals,
Merck, Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche
Diagnostics, and Sanofi; and payment or honoraria for lectures,presentations, speakers bureaus, manuscript writing, or educational
events from Abbott Laboratories, AstraZeneca, Boehringer Ingelheim,
Eli Lilly, Janssen Pharmaceuticals, and Novo Nordisk. PJN reports
grants from NIH. PMR has received institutional research grantsupport from Kowa Pharmaceuticals, Novartis, Amarin, Pfizer,
Esperion, Novo Nordisk, and the NHLBI; has served as a consultant toNovartis, AstraZeneca, Kowa Pharmaceuticals, and Novo Nordisk; andreceives compensation for service on the Peter Munk Advisory Board(University of Toronto, Toronto, ON, Canada), the Leducq Foundation,and the Baim Institute (Boston, MA, USA). RTdJ reports grants fromTakeda Pharmaceuticals and payment or honoraria for lectures,presentations, speakers bureaus, manuscript writing, or educational
events from Amgen. SGT has received institutional research grantsupport from the BHF. SJG is a trustee of the Novo Nordisk UK
Research Foundation medical research charity. SKi reports grants orcontracts from VASCage (Research Centre on Vascular Ageing andStroke, project number 868624) of the Austrian Research Promotion
Agency FFG (COMET program–Competence Centers for Excellent
Technologies) funded by the Federal Ministry for Climate Protection,
Environment, Energy, Transport, Innovation and Technology; theFederal Ministry for Labour and Economy; and the federal states Tyrol(via Standortagentur), Salzburg, and Vienna (via Vienna BusinessAgency). SK has received institutional research grant support from the
BHF, the UK MRC, the UK NIHR, and Health Data Research UK.
SB has received institutional research grant support from the Wellcome
Trust and the MRC. SWS reports receiving institutional grants fromthe NIH, royalties from Springer Publishing, and consulting fees fromFred Hutchinson Cancer Institute and State University of NY at
Buffalo. TS reports consulting fees from Amarin, Amgen, Novartis,
Orion Pharma, Raisio Group, and Sankyo; and a leadership or fiduciaryrole for the Finnish guidelines for dyslipidaemia. VS reports receiving
institutional grants from Juho Vainio Foundation and Bayer. All otherauthors declare no competing interests.
Data sharing
Data from the UK Biobank are available to any legitimate scientificresearch on application. Data from the Emerging Risk Factors
Collaboration are available at the discretion of the principal investigatorsof the individual studies.
Acknowledgements
This research was conducted using the UK Biobank Resource underapplication number 13784. No specific funding was obtained for thismanuscript. The coordinating centre was supported by core funding
from the British Heart Foundation (RG/18/13/33946), Cambridge BHF
Centre of Research Excellence (RE/18/1/34212) and BHF Chair Award(CH/12/2/29428), and NIHR Cambridge Biomedical Research Centre(BRC-1215-20014; NIHR203312). JD holds a BHF Professorship and an
NIHR Senior Investigator Award. AMW is part of the BigData@Heart
Consortium, funded by the Innovative Medicines Initiative-2 Joint
Undertaking under grant agreement 116074 and is supported by the
BHF-Turing Cardiovascular Data Science Award (BCDSA\100005).
MI is supported by the Munz Chair of Cardiovascular Prediction andPrevention and by the UK Economic and Social Research 878 Council(ES/T013192/1). EDA holds a NIHR Senior Investigator Award. The
views expressed are those of the authors and not necessarily those of
the NIHR or the Department of Health and Social Care.
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