Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation

Kaptoge, Stephen; Seshasai, Sreenivasa Rao Kondapally; Sun, Liang; Walker, Mark; Bolton, Tom; Spackman, Sue; Ataklte, Feven; Bell, Steven; Burgess, Stephen; Pennells, Lisa; Altay, Servet; Best, LG; Björkelund, Cecilia; Blazer, Dan G.; Brunner, Eric J.; Gr, Dagenais; D’Agostino, RB; Daimon, Makoto; Daniels, Lynne; Danker, Rosie; Davidson, K.; Jongh, RT de; Ducimetière, Pierre; Elders, Petra; Ford, Ian; Gallacher, I; Bakker, S. J. L.; Goldbourt, Uri; Camara, G.N.N.L.; Grimsgaard, Sameline; Gudnason, Vilmundur; Hansson, Per‐Olof; Imano, Hironori; Jukema, J. Wouter; Kabrhel, Christopher; Kauhanen, Jussi; Kavousi, Maryam; Kiechl, Stefan; Krumholz, HM; Lh, Kuller; Lowler, DA; Meyer, H W; Mukamal, Kenneth J.; Nietert, Paul J.; Ninomiya, Toshiharu; Nordestgaard, Børge G.; Price, JF; Pm, Ridker; Sun, Qi; Roussel, Ronan; Sakurai, Masaru; Schöttker, Ben; Shaw, J R; Strandberg, T. E.; Tolonen, Hanna; Wagenknecht, Lynne E.; Rb, Wallace; Wannamethee, SG; Wareham, NJ; Wassertheil‐Smoller, Sylvia; Yeap, BB; Harrison, Seamus C.; Inouye, Michael; Griffin, Simon J.; Butterworth, Adam S.; Wood, Angela; Thompson, SG; Danesh, John; Angelantonio, Emanuele Di; Tipping, Robert; Russell, Sarah; Johansen, Michelle C; Bancks, Michael P.; Mongraw‐Chaffin, Morgana; Magliano, Dianna J.; Barr, Elizabeth L. M.; Zimmet, P; Knuiman, MW; Whincup, Peter H.; Willeit, Johann; Willeit, Peter; Leitner, Christoph; Lawlor, Debbie A.; Ben-Shlomo, Yoav; Pc, Elwood; Sutherland, SE; Kj, Hunt; Cushman, Mary; Selmer, RM; Håheim, Lise Lund; Ariansen, Inger; Tybjaer-Hansen, A; Frikkle-Schmidt, R; Langsted, Anne; Donfrancesco, Chiara; Noce, Cinzia Lo; Balkau, Beverley; Bonnet, Fabrice; Fumeron, Frédéric; Pablos, David Lora; Ferro, Charles J.; Morales, T Julieth Parra; McLachlan, Stela; Guralnik, Jack M.; Khaw, K-T; Brugger, Hermann; Holleczek, Bernd; Stocker, Hannah; Nissinen, Aulikki; Palmieri, Luigi; Vartiainen, Erkki; Jousilahti, Pekka; Harald, Kennet; Massaro, JM; Pencina, Michael J.; Lyass, Asya; Susa, Shinji; Oizumi, Toshihide; Kayama, Takamasa; Chetrit, Angela; Roth, Jesse; Orenstein, Liat; Welin, Lennart; Svärdsudd, Kurt; Lissner, Lauren; Hange, Dominique; Mehlig, Kirsten; Salomaa, Veikko; Tilvis, R; Dennison, Elaine; Cooper, Cyrus; Westbury, Leo; Norman, PE; Almeida, OP; Hankey, Graeme J.; Hata, Jun; Shibata, Mamoru; Furuta, Yoshihiko; Bom, Michiel J.; Rutters, Femke; Muilwijk, Mirthe; Kraft, Peter; Lindström, Sara; Turman, Constance; Yamagishi, Kazumasa; Gerber, Yariv; Laatikainen, Tiina; Salonen, J. T.; Schoor, LN van; Zutphen, EM van; Verschuren, W.M.M.; Engström, Gunnar; Melander, Olle; Psaty, BM; Blaha, Michael J.; Boer, Irene de; Kronmal, R A; Sattar, Naveed; Rosengren, Annika; Nitsch, Dorothea; Grandits, Greg; Tverdal, Aage; Shin, Hyung‐Joon; Albertorio, JR; Gillum, R. F.; Hu, Fang; Cooper, J.A.; Humphries, Steve E.; Briggs, Farren; Vrany, Elizabeth A.; Butler, Melissa G.; Schwartz, Joel; Kiyama, Masahiko; Kitamura, Akihiko; Iso, Hiroyasu; Amouyel, Philippe; Arveiler, Dominique; Ferrières, Jean; Gansevoort, Ron T.; Boer, Rudolf A. de; Kieneker, Lyanne M.; Crespo, Carlos; Assmann, Gerd; Trompet, Stella; Kearney, Patricia M.; Cantin, Bernard; Després, J.; Lamarche, Benoı̂t; Laughlin, Gail A.; McEvoy, Linda K.; Aspelund, Thor; Þórsson, Bolli; Sigurðsson, Gunnar; Tilly, Martijn J; Ikram, M. Arfan; Dörr, Marcus; Schipf, Sabine; Völzke, Henry; Fretts, Amanda M.; Umans, JG; Ali, Tauqeer; Shara, Nawar; Smith, George Davey; Can, Günay; Yüksel, Hüsniye; Özkan, Uğur; Nakagawa, H; Morikawa, Yuko; Ishizaki, Masao; Njølstad, Inger; Wilsgaard, Tom; Mathiesen, Ellisiv B.; Sundström, Johan; Buring, Julie E.; Cook, Nancy R.; Arndt, Volker; Rothenbacher, Dietrich; Manson, JoAnn E.; Tinker, Lesley F.; Shipley, Martin J.; Tabak, AG; Kivimäki, Mika; Packard, Chris J.; Robertson, Michele; Feskens, Edith J. M.; Geleijnse, Johanna M.; Kromhout, Daan

doi:10.1016/s2213-8587(23)00223-1

Cited by 70 publications

(18 citation statements)

References 50 publications

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“…The association of adolescent CRF with work ability in adulthood is likely due to the interrelationship between CRF and chronic cardiometabolic conditions that negatively influence work ability . It is widely accepted that in adulthood CRF is an important individual factor associated with cardiometabolic disease, which is a major burden for public health globally . Moreover, CRF in youth may contribute to the risk for metabolic syndrome, cardiovascular disease, and type 2 diabetes later in life.…”

Section: Discussionmentioning

confidence: 99%

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Adolescent Cardiorespiratory Fitness and Future Work Ability

Laakso,

Ortega,

Huotari

et al. 2024

JAMA Netw Open

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ImportanceAlthough research indicates that low fitness in youth is associated with a higher risk of chronic disability in men, the association of fitness in adolescence with work ability in working men and women remains unknown.ObjectiveTo examine the associations of adolescent health-related physical fitness with future work ability.Design, Setting, and ParticipantsThis 45-year observational cohort study, conducted in Finland, examined the direct and indirect longitudinal associations of objectively measured health-related fitness in adolescence (in 1976) with self-reported work ability and sickness absence in early middle age (in 2001) and the Work Ability Index in late middle age (in 2021). A countrywide stratified random baseline sample included fitness measurements for cardiorespiratory fitness (CRF) (running 1.5 km for girls and 2 km for boys), musculoskeletal fitness (MF; standing broad jump and sit-ups for both sexes, pull-ups for boys, and flexed-arm hang for girls), and height and weight, from which body mass index (BMI) was calculated. Structural equation modeling–based path analysis, adjusted for age and sex at baseline and for educational level, work-related physical strain, and leisure-time physical activity in late middle age, was conducted. Data analysis was performed from January to July 2023.Main Outcomes and MeasuresSelf-reported work ability was measured with structured questions in early middle age and with the validated Work Ability Index in late middle age.ResultsThe final sample from longitudinal analyses (1207 individuals; 579 [48%] male individuals) consisted of participants with fitness measurement from age 12 to 19 years, and work ability assessment from age 37 to 44 years and/or age 57 to 64 years. Higher adolescent CRF was associated with higher work ability (839 participants; β = 0.12; 95% CI, 0.01 to 0.22; P = .03) and lower sickness absence (834 participants; β = −0.07; 95% CI, −0.12 to −0.02; P = .004) in early middle age and, indirectly, mediated by work ability in early middle age, with a higher work ability at the end of working age (603 participants; β = 0.04; 95% CI, 0.001 to 0.08; P = .04). The results remained consistent in both sexes and after adjustment for the confounders. Neither MF nor BMI was associated with work ability (MF, 1192 participants; β = −0.07; 95% CI, −0.17 to 0.03; BMI, 1207 participants, β = 0.09; 95% CI, −0.004 to 0.19) or sickness absence (MF, 1185 participants, β = 0.02; 95% CI, −0.03 to 0.06; BMI, 1202 participants, β = −0.03; 95% CI, −0.09 to 0.03) in early middle age or with late middle age work ability, mediated by work ability in early middle age (MF, 603 participants, β = −0.02; 95% CI, −0.06 to 0.01; BMI, 603 participants, β = 0.03; 95% CI, −0.004 to 0.07).Conclusions and RelevanceThese findings suggest that low CRF in youth is associated with poor work ability at the middle and end of working life, which highlights the informative and prognostic value of CRF assessment early in youth. Enhancing CRF in the first decades of life might contribute to better work capacity and productivity in the labor force, which would have implications for health, quality of life, society, and the economy.

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Section: Discussionmentioning

confidence: 99%

“…The total WAI score ranges between 7 and 49. Scores can subsequently be categorized according to the original classification 4 as poor , average (28)(29)(30)(31)(32)(33)(34)(35)(36), good (37)(38)(39)(40)(41)(42)(43), and very good (44)(45)(46)(47)(48)(49).…”

Section: Assessment Of Work Abilitymentioning

confidence: 99%

Adolescent Cardiorespiratory Fitness and Future Work Ability

Laakso,

Ortega,

Huotari

et al. 2024

JAMA Netw Open

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“…The risk of death and complications was calculated using Cox regression, adjusting sex and race in the basic model, and further adjusting GRS, education, TDI, and air pollution in the additional model. Years of life lost were calculated as the difference in the areas under the survival curves of each cluster [17][18][19] . Hazard ratios for death at specified age (interactions between age and cluster were added in Cox regression), prevalence of each AF cluster by age, and population all-cause mortality rates (driven from the Office for National Statistics 20 ) were used to estimate reductions in life expectancy associated with AF cluster 17 .…”

Section: Characterization and Prognostic Analysismentioning

confidence: 99%

“…Years of life lost were calculated as the difference in the areas under the survival curves of each cluster [17][18][19] . Hazard ratios for death at specified age (interactions between age and cluster were added in Cox regression), prevalence of each AF cluster by age, and population all-cause mortality rates (driven from the Office for National Statistics 20 ) were used to estimate reductions in life expectancy associated with AF cluster 17 . In addition, we also used 491,291 participants without AF within two years after baseline as the reference in the supplementary analysis.…”

Section: Characterization and Prognostic Analysismentioning

confidence: 99%

Outcome and life expectancy associated with novel subgroups of atrial fibrillation: a data-driven cluster analysis

Yu,

Li,

Sun

et al. 2023

Preprint

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AimThis study aimed to develop a refined classification of atrial fibrillation (AF) according to comprehensive risk factors to identify patients at high-risk for poor prognosis and their life expectancy.MethodA total of 7,391 participants aged 40-69 with AF at baseline, from UK Biobank, were classified into five clusters, based on seven clustering variables including age and six risk factor categories (metabolic disease, respiratory disease, cardiovascular disease, renal/immune-mediated disease, mental health disease, acute illness). Difference in the risk of death and major complications, as well as reductions in life expectancy, among clusters were estimated. Replication was done in 2,399 participants with newly diagnosed AF within two years after baseline.ResultsFive distinct AF clusters were identified: acute illness-related, mental health-related, cardiovascular disease-related, immune-and-renal disease-related, and respiratory-and- metabolic disease-related AF. Patients with respiratory-and-metabolic disease-related AF had the highest risk of death, acute myocardial infarction, heart failure, and cerebral ischemic stroke, while those with acute illness-related AF had the lowest corresponding risk. In addition, compared with individuals with acute illness-related AF, those with respiratory-and- metabolic disease-related and mental health-related AF had the top 2 greatest loss of life expectancy. Furthermore, genetic variants for AF had different effect among the five clusters. Replication analysis confirmed the result stability.ConclusionA novel AF classification was developed, which provided insights into varying life expectancy and risks of death and complications among AF subgroups with distinct characteristics. It offers a practical approach for identifying high-risk patients, which might help to tailor precise interventions for AF management.

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“…Patients with obesity and type 2 diabetes do not have the luxury of waiting until 2041 for comprehensive coverage of metabolic surgery. One recent study led by the Emerging Risk Factors Collaboration found that each decade of living with type 2 diabetes shortens a patient’s lifespan by nearly 4 years . It has already been 32 years since the Pories et al report on gastric bypass and type 2 diabetes.…”

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confidence: 99%

Metabolic Surgery for Diabetes Management

Kindel,

Funk,

Ghaferi

2024

JAMA Surg

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The debate regarding the effectiveness of metabolic surgery compared to medical therapy for type 2 diabetes started more than 3 decades ago with the insight of Pories and colleagues in their revolutionary article "Is Type II Diabetes Mellitus (NIDDM) A Surgical Disease?" 1 In this report, the authors noted that gastric bypass surgery provided exceptional glycemic control for patients with non-insulin dependent type 2 diabetes. Following these foundational observations, there have been multiple prospective trials that have clearly delineated the superior effectiveness of metabolic surgery for glycemic outcomes in patients with type 2 diabetes and overweight or obesity. [2][3][4] For instance, the Surgical Treatment and Medications Potentially Eradicate Diabetes Efficiently (STAMPEDE) trial 2,3 randomized patients with type 2 diabetes to intensive medical therapy or intensive medical therapy with metabolic surgery. This trial demonstrated that metabolic surgery was superior to intensive medical therapy at 3 and 5 years for glycemic outcomes, including reaching hemoglobin A 1C (HgbA 1C ) goals established by the American Diabetes Association. The importance of achieving these HgbA 1C thresholds with metabolic surgery is these outcomes translate to reduced longterm cardiovascular complications, like heart failure and coronary artery disease. In a cohort study of nearly 50 000 patients with type 2 diabetes, each 1% increase in HbA 1C was associated with an 8% increased risk of new heart failure. 5 The total annual cost of diabetes care in the US in 2022 was $412.9 billion, with $106.3 billion (26%) attributed to lost productivity at work, unemployment from chronic disability, and premature mortality. 6 Conversely, metabolic surgery in patients with type 2 diabetes compared to historical control individuals receiving usual care was associated with a reduction in the likelihood of all-cause mortality by 39% at 8 years 4 and has been demonstrated to be cost effective in numerous studies with increasing savings over longer periods. 7 In 2017, the 2nd Diabetes Surgery Summit published recommendations for the use of metabolic surgery for type 2 diabetes following consensus-guideline development from 48 international experts (surgeons and nonsurgeons) and multiple diabetes-focused societies including the American Diabetes Association. 8 These recommendations included unanimous support to include metabolic surgery as a treatment option for type 2 diabetes for patients with class 3 obesity (body mass index [BMI] ≥40, calculated as weight in kilograms divided by height in meters squared). Metabolic surgery was also recommended for patients with type 2 diabetes and class 2 obesity (BMI 35.0-39.9) with inadequately controlled hyperglycemia despite medical therapy. Furthermore, the delegates concluded with 87% consensus that metabolic surgery should be considered to treat type 2 diabetes in patients with class 1 obe-Opinion EDITORIAL

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Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation

Cited by 70 publications

References 50 publications

Adolescent Cardiorespiratory Fitness and Future Work Ability

Adolescent Cardiorespiratory Fitness and Future Work Ability

Outcome and life expectancy associated with novel subgroups of atrial fibrillation: a data-driven cluster analysis

Metabolic Surgery for Diabetes Management

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