Table 2.
Summary of AD studies by risk factor
| Risk Factor | Short reference | Exposure measure | Age group* | RR | I2 (%) | Bias (Egger’s p) | n |
| Demographics | |||||||
| Education | Xu 2016 [19] | Lowest versus reference quartile | adj | 1.78 (1.43, 2.22) | 36.0 | absent∧ | 9 |
| Xu 2015 [20] | Low (<16 y) versus high (≥16 y) | adj | 1.60 (1.32–1.94) | 57.0 | 0.00 | 14 | |
| Caamano-Isorna 2006 [18] | Lower versus highest levels | adj | 1.32 (1.09, 1.59) | absent | – | 9 | |
| Xu 2016 [19] | Highest versus reference quartile | adj | 0.44 (0.32, 0.60) | 41.5 | 0.018 | 10 | |
| Lifestyle | |||||||
| Alcohol | Drinker versus non-drinkers | ||||||
| Anstey 2009 [27] | Drinker versus non-drinkers | LL | 0.66 (0.47, 0.94) | 0.0 | ∼ | 2 | |
| Xu 2015 [20] | Ever versus never | LL/? | 0.43 (0.17, 0.69) | 0.0 | 0.33 | 3 | |
| Anstey 2009 [27] | Heavy/excessive versus non-drinker | LL | 0.92 (0.59, 1.45) | 0.0 | 0.22 | 3 | |
| Xu 2015 [20] | High versus low/none | LL/? | 0.96 (0.18, 1.74) | 78.8 | 0.56 | 3 | |
| Xu 2015 [20] | Light-moderate consumption versus non-drinkers | LL/? | 0.61 (0.54, 0.68) | 0.0 | 0.44 | 5 | |
| Anstey 2009 [27] | Light to moderate versus non-drinker | LL | 0.72 (0.61, 0.86) | 56.4 | 0.36 | 6 | |
| Cognitive engagement | Xu 2015 [20] | High participation in cognitive activity | LL/? | 0.53 (0.42, 0.63) | 90.5 | 0.00 | 5 |
| Diet | Singh 2014 [50] | Adherence to Mediterranean diet-highest versus lowest | LL | 0.64 (0.46, 0.89) | 0.0 | ∼ | 2 |
| Xu 2015 [20] | Caffeine/coffee drinking | ML/? | 0.69 (0.47, 0.90) | 0.0 | 0.96 | 3 | |
| Wu 2016 [51] | <1 cup coffee per day versus 1-2 cups | LL | 0.71 (0.54, 0.94) | 0.0 | 0.98 | 3 | |
| Kim 2015 [52] | Coffee intake-highest versus lowest | LL | 0.71 (0.52, 0.97) | 0.0 | ∼ | 3 | |
| Liu 2016 [32] | Coffee intake-highest versus lowest | ML/LL | 0.73 (0.55, 0.97) | 0.0 | 0.80 | 4 | |
| Barranco 2007 [53] | Coffee consumption versus non-consumption | ? | 0.73 (0.54, 0.99) | 0.0 | ∼ | 2 | |
| Xu 2015 [20] | Fat, DHA | LL/? | 0.76 (0.52, 1.11) | 68.3 | 0.04 | 4 | |
| Wu 2015 [30] | Fat, DHA/EPA-highest versus lowest | LL | 0.89 (0.74, 1.08) | 36.3 | 0.01 | 3 | |
| Xu 2015 [20] | Fat, EPA | ? | 0.96 (0.75, 1.16) | 0.0 | 0.25 | 3 | |
| Zhang 2016 [31] | Fat, DHA-0.1-g/d increment | ML/LL | 0.63 (0.51, 0.76) | 94.6 | 0.10 | 3 | |
| Zhang 2016 [31] | Fat, PUFA-8-g/d increment | ML/LL | 0.96 (0.65, 1.27) | 34.6% | – | 2 | |
| Zhang 2016 [31] | Fat, EPA-0.1-g/d increment | ML/LL | 1.04 (0.85, 1.23) | 5.1 | 0.10 | 2 | |
| Wu 2015 [30] | Fish intake-highest versus lowest | LL | 0.64 (0.44, 0.92) | 59.0 | 0.10 | 6 | |
| Xu 2015 [20] | Fish intake | LL/? | 0.66 (0.43, 0.90) | 64.7% | 0.54 | 6 | |
| Zhang 2016 [31] | Fish-increment of 1 serving/wk | ML/LL | 0.93 (0.90, 0.95) | 74.8% | 0.174 | 5 | |
| Xu 2015 [20] | Folate-high serum folate levels | LL/? | 0.51 (0.29, 0.73) | 16.0% | 0.29 | 4 | |
| Kim 2015 [52] | Tea intake-highest versus lowest | LL | 1.12 (0.83, 1.50) | 0.0% | ∼ | 3 | |
| Xu 2015 [20] | Vitamin C intake | LL/? | 0.74 (0.55, 0.93) | 0.0% | 0.19 | 6 | |
| Xu 2015 [20] | Vitamin E intake | LL/? | 0.73 (0.62, 0.84) | 0.0% | 0.81 | 6 | |
| Shen 2015 [54] | Vitamin D deficiency (25(OH)D level < 50 nmol/L) | LL/? | 1.21 (1.02, 1.41) | 0.0% | – | 2 | |
| Physical activity | Santos-Lozano 2016 [55] | Physically active (according to international PA guidelines:>150 min/week of MVPA) versus inactive | LL | 0.60 (0.51, 0.71) | 5.6% | 0.34 | 5 |
| Xu 2015 [20] | High participation in leisure-time PA | LL/? | 0.65 (0.46, 0.84) | 81.0% | 0.09 | 10 | |
| Santos-Lozano 2016 [55] | Higher versus lower PA | ML/LL | 0.65 (0.55, 0.75) | 39.3% | 0.83 | 9 | |
| Daviglus 2011 [56] | Higher versus lower PA | ? | 0.72 (0.53, 0.98) | – | – | 9 | |
| Xu 2017 [29] | Higher versus lower PA | ML/LL | 0.80 (0.69, 0.94) | 0.0% | ∼ | 8 | |
| Hamer 2009 [28] | Highest versus lowest PA | ML/LL | 0.55 (0.36, 0.84) | 79.5% | <0.01 | 6 | |
| Beckett 2015 [57] | Highest versus lowest PA | ML | 0.61 (0.52, 0.73) | 0.0% | 0.02 | 9 | |
| Xu 2017 [29] | Highest versus lowest PA | ML/LL | 0.74 (0.58, 0.94) | 46.3% | ∼ | 8 | |
| Sleep | Bubu 2016 [58] | All sleep problems/disorders listed in International Classification of Sleep Disorders versus none | ML/LL | 1.47 (1.28, 1.69) | 66.9% | 0.79 | 6 |
| Smoking | Zhong 2015 [22] | Current versus never | LL | 1.40 (1.13, 1.73) | 66.8% | <0.01 | 12 |
| Anstey 2007 [49] | Current versus former | LL/? | 1.70 (1.25, 2.31) | 0.0% | 0.70 | 4 | |
| Anstey 2007 [49] | Current versus never | LL/? | 1.79 (1.43, 2.23) | 0.0% | 0.89 | 4 | |
| Almeida 2002 [23] | Current versus never/non-smokers | ? | 1.99 (1.33, 2.98) | 56.5% | ∼ | 7 | |
| Peters 2008 [59] | Current versus never/non-smokers | ML/LL/? | 1.59 (1.15, 2.20) | 69.9% | 0.19 | 8 | |
| Zhong 2015 [22] | Ever versus never | LL | 1.12 (1.00, 1.26) | 55.9% | <0.01 | 23 | |
| Almeida 2002 [23] | Ever versus never | ? | 1.10 (0.94, 1.29) | 93.5% | 0.53 | 7 | |
| Zhong 2015 [22] | Former versus never | LL | 1.04 (0.96, 1.13) | 2.8% | <0.01 | 13 | |
| Xu 2015 [20] | Former versus never | 1.00 (0.92, 1.08) | 0.0% | 0.27 | 9 | ||
| Peters 2008 [59] | Former versus never | ? | 0.99 (0.81, 1.23) | 46.8% | 0.79 | 8 | |
| Medical | |||||||
| Arthritis | Xu 2015 [20] | History of arthritis (self-report) | LL/? | 0.63 (0.42, 0.84) | 0.0% | 0.83 | 2 |
| Atrial fibrillation | Kalantarian 2013 [60] | Yes versus no (ECG, medical history, ICD-9, unclear) | LL | 1.47 (0.92, 2.34) | 68.2% | ∼ | 3 |
| Xu 2015 [20] | Yes versus no (medical records, self-report health questionnaire) | LL | 1.29 (0.97, 1.60) | 60.6% | 0.94 | 3 | |
| BMI | Anstey 2011 [17] | Change (increase) continuous measures of BMI | LL | 0.72 (0.62, 0.84) | 71.5% | ∼ | 2 |
| Xu 2015 [20] | High BMI (>28/30) in midlife versus normal | ML/LL/? | 1.61 (1.11, 2.12) | 69.2% | 0.11 | 6 | |
| Xu 2015 [20] | High BMI (>25–30/abdominal obesity/BMI increase) in late-life | LL/? | 0.80 (0.64, 0.97) | 72.9% | 0.95 | 12 | |
| Anstey 2011 [17] | Obese versus normal | ML/LL | 2.04 (1.59, 2.69) | 82.8% | ∼ | 3 | |
| Loef 2013 [44] | Obese versus normal | ML/LL | 1.98 (1.24, 3.14) | – | – | 4 | |
| Meng 2014 [61] | Obese versus normal | ML | 1.88 (1.32, 2.69) | 59.1% | 0.55 | 5 | |
| Beydoun 2008 [45] | Obese versus normal | ML/LL | 1.80 (1.00, 3.29) | – | <0.01 | 4 | |
| Anstey 2011 [17] | Obese versus not Obese | LL | 1.46 (0.97, 2.21) | 42.3% | ∼ | 2 | |
| Anstey 2011 [17] | Overweight versus normal | ML/LL | 1.35 (1.19, 1.54) | 92.0% | ∼ | 3 | |
| Loef 2013 [44] | Overweight versus normal | ML/LL | 1.44 (0.96, 2.15) | – | – | 4 | |
| Anstey 2011 [17] | Underweight versus normal | ML/LL | 1.96 (1.32, 2.92) | 69.1% | ∼ | 3 | |
| Cancer | Ma 2014 [62] | History of cancer versus none (ICD code diagnosis) | LL | 0.63 (0.56, 0.72) | 0.0% | 0.28 | 5 |
| Xu 2015 [20] | Yes versus no (Questionnaire/self-report, ASL-Mi1 tumor registry) | LL/? | 0.65 (0.57, 0.73) | 6.7% | 0.81 | 6 | |
| Carotid atherosclerosis | Xu 2015 [20] | Yes versus no (carotid medina wall thickness) | 1.65 (1.03, 2.26) | 31.1% | ∼ | 2 | |
| Cholesterol | Anstey 2017 [63] | High cholesterol (>6.5 mmol/l) versus non-high-midlife | ML | 2.14 (1.33, 3.44) | 12.9% | ∼ | 3 |
| Meng 2014 [61] | High cholesterol (>6.5 mmol/l) versus non-high | ML | 1.72 (1.32, 2.24) | 8.5% | possible∧ | 4 | |
| Xu 2015 [20] | Elevated serum total cholesterol level | ML/LL/? | 1.07 (0.89, 1.28) | 59.9% | 0.02 | 16 | |
| Daviglus 2011 [56] | Highest versus lowest quartile | ? | 0.85 (0.65, 1.12) | – | ∼ | 3 | |
| Anstey 2017 [63] | Highest versus lowest quartile-Total cholesterol, late-life | LL | 0.93 (0.69, 1.26) | 50.5% | 0.28 | 4 | |
| Anstey 2017 [63] | Low HDL-C | LL | 0.78 (0.54, 1.13) | 65.4% | ∼ | 3 | |
| Anstey 2008 [17] | Second versus lowest quartile-total cholesterol | LL | 0.85 (0.67, 1.10) | 40.1% | ∼ | 3 | |
| Depression | Cherbuin 2015 [14] | Categorical clinical thresholds (>20/21 CES-D or equivalent) | LL | 2.04 (1.40, 2.98) | 54.9% | possible∧ | 10 |
| Diniz 2013 [24] | Continuous (mostly CES-D &variants) | ? | 1.65 (1.42, 1.92) | 2.0% | absent∧ | 17 | |
| Xu 2015 [20] | Continuous (self-reporting, CES-D, HAM, Questionnaire, DSM-IV, Diagnosis, CAMDEX, Neuropsychiatric interview, SCL-90) | LL/? | 1.08 (1.04, 1.13) | 40.3% | 0.00 | 24 | |
| Cherbuin 2015 [14] | Continuous symptomology measures-CES-D, HAM, GDS, SCL-90, the NEO | LL | 1.06 (1.02, 1.10) | 62.1% | possible∧ | 10 | |
| Diabetes | Zhang 2017 [64] | Any diabetes (Type I or II) | ? | 1.53 (1.42, 1.63) | 18.5% | absent∧ | 17 |
| Meng 2014 [61] | Any diabetes (Type I or II) | ML/LL | 1.40 (1.25, 1.57) | 10.6% | – | 4 | |
| Vagelatos 2013 [16] | Type II diabetes, self-report and blood sampling | ML/LL | 1.57 (1.41, 1.75) | 38.7% | 0.22 | 15 | |
| Gudala 2013 [65] | Type II diabetes (self-reported, registry-based/antidiabetics use) | ML/LL | 1.56 (1.41, 1.73) | 9.8% | 0.93 | 20 | |
| Cheng 2012 [48] | Type II diabetes (according to standard criteria) | ML/LL | 1.54 (1.40, 1.70) | 71.7% | <0.01 | 18 | |
| Lu 2009 [15] | Type II diabetes (medical history, laboratory test, antidiabetic medications) | LL | 1.39 (1.16, 1.66) | 0.0% | <0.01 | 8 | |
| Xu 2015 [20] | Type II diabetes (self-report, family report) | ML/LL | 1.33 (1.14, 1.52) | 70.4% | 0.06 | 22 | |
| Vagelatos 2013 [16] | Type II diabetes, self-report and blood sampling | ML/LL | 1.57 (1.41, 1.75) | 38.7% | 0.22 | 15 | |
| Homocysteine | Van Dam 2009 [21] | Hyperhomocysteinema | LL | 2.50 (1.38, 4.56) | 81.6% | ∼ | 3 |
| Xu 2015 [20] | High total homocysteine levels | ML/LL/? | 1.15 (1.09, 1.23) | 45.0% | 0.00 | 8 | |
| Hormones | Wang 2016 [66] | High versus normal levels of thyrotropin | LL | 1.70 (1.18, 2.45) | 42.2% | 0.75 | 2 |
| Wang 2016 [66] | Low versus normal levels of thyrotropin | LL | 1.69 (1.31, 2.19) | 38.0% | 0.74 | 4 | |
| Lv 2016 [67] | Low plasma testosterone (in elderly men) | ? | 1.48 (1.12, 1.96) | 47.2% | 0.15 | 7 | |
| Wang 2016 [66] | Per SD increment in thyrotropin levels | LL | 0.89 (0.78, 1.01) | 31.3% | 0.01 | 6 | |
| Hyper/Hypotension | Meng 2014 [61] | All combined-high SBP, DBP, hypertension | ML/LL | 1.31 (1.01, 1.70) | 45.7% | – | 5 |
| Meng 2014 [61] | High DBP | ML/LL | 2.38 (1.34, 4.23) | 0.0% | – | 3 | |
| Meng 2014 [61] | High SBP | ML/LL | 1.77 (0.93, 3.37) | 0.0% | – | 3 | |
| Xu 2015 [20] | Higher SBP | ? | 1.02 (0.92, 1.13) | 68.7% | <0.01 | 28 | |
| Meng 2014 [61] | Hypertension versus none | ML/LL | 1.10 (0.88, 1.37) | 48.6% | – | 2 | |
| Guan 2011 [65] | Hypertension versus none | ML/LL | 1.01 (0.87, 1.18) | 37.2% | – | 9 | |
| Xu 2015 [20] | Lower DBP | LL/? | 1.14 (0.89, 1.39) | 60.0% | <0.01 | 6 | |
| Power 2011 [68] | Per 10 mmHg DBP | ML | 0.93 (0.84, 1.04) | 12.4% | 0.85 | 4 | |
| Power 2011 [68] | Per 10 mmHg DBP | LL | 0.94 (0.85, 1.04) | 14.0% | 0.45 | 5 | |
| Power 2011 [68] | Per 10 mmHg increment SBP | ML | 0.95 (0.90, 1.00) | 69.4% | ∼ | 4 | |
| Power 2011 [68] | Per 10 mmHg increment SBP | LL | 0.95 (0.91, 1.00) | 0.0% | 0.54 | 5 | |
| Sharp 2011 [69] | History of/current hypertension | ? | 1.59 (1.29, 1.95) | 37.4% | <0.01 | 6 | |
| Power 2011 [68] | History of hypertension | ML/LL | 0.98 (0.80, 1.19) | 41.8% | 0.69 | 12 | |
| Inflammatory markers | Koyama 2013 [70] | C-reactive protein | LL | 1.36 (1.13, 1.63) | 40.3% | ∼ | 3 |
| Koyama 2013 [70] | Interleukin-6 | LL | 1.15 (0.84, 1.59) | 0.0% | ∼ | 4 | |
| Metabolic syndrome | Xu 2015 [20] | NCEP ATP III criteria | LL/? | 0.71 (0.49, 0.93) | 36.5% | 0.30 | 4 |
| Peripheral artery disease | Xu 2015 [20] | Ankle to Brachial Index < 0.9–11 | LL/? | 1.68 (0.97, 2.38) | 0.0% | 0.51 | 2 |
| Renal Disease | Xu 2015 [20] | eGFR (MDRD), I/SCr, questionnaire | LL/? | 1.13 (0.68, 1.59) | 0.0% | 0.67 | 3 |
| Serum uric acid | Du 2016 [71] | Serum uric acid levels | ? | 0.66 (0.52, 0.85) | 6.0% | low risk∧ | 3 |
| Stroke | Xu 2015 [20] | Self-reported history of stroke | LL/? | 0.97 (0.71, 1.24) | 40.9% | 0.03 | –9 |
| Zhou 2015 [72] | Stroke diagnosis based on the International Classification of Diseases | LL | 1.59 (1.25, 2.02) | 0.0% | ∼ | 5 | |
| TBI | Xu 2015 [20] | Head trauma with/without loss of consciousness | LL/? | 1.18 (0.89, 1.47) | 7.5% | 0.16 | 6 |
| Li 2017 [73] | Prior TBI | LL/? | 1.24 (1.04, 1.49) | 26.8 | 0.32 | 8 | |
| Perry 2016 [74] | Prior TBI | ? | 0.95 (0.58, 1.54) | 51.4% | 0.83 | 7 | |
| Pharmacological | |||||||
| Antacids | Virk 2015 [75] | Aluminum containing antacids | ? | 0.70 (0.30, 1.80) | 0.0% | ns | 2 |
| Virk 2015 [75] | Antacid | ? | 0.83 (0.39, 1.78) | 0.0% | ns | 2 | |
| Antihypertensives | Xu 2015 [20] | Anti-hypertensives | LL/? | 0.71 (0.59, 0.83) | 52.7% | 0.36 | 5 |
| Xu 2017 [38] | Anti-hypertensives | LL | 0.83 (0.64, 1.07) | 40.5% | possible∧ | 6 | |
| Chang-Quan 2011 [76] | Anti-hypertensives | ML/LL/? | 0.92 (0.79, 1.08) | 0.0% | 0.66 | 5 | |
| Guan 2011 [77] | Anti-hypertensives | ML/LL | 0.92 (0.79, 1.08) | 0.0% | 0.66 | 5 | |
| Anti-inflammatories | Wang 2015 [78] | Aspirin | LL/? | 0.74 (0.57, 0.97) | 67.9% | – | 8 |
| Etminan 2003 [79] | Aspirin | ML/LL | 0.85 (0.71, 1.03) | 80.5% | 0.90 | 5 | |
| Wang 2015 [78] | Non-aspirin NSAIDs | LL/? | 0.61 (0.43, 0.88) | 68.6% | 0.04 | 7 | |
| Szekely 2004 [25] | NSAIDs-exposure for 2 or more years | ML/LL/? | 0.42 (0.26, 0.66) | 0.0% | ∼ | 3 | |
| Xu 2015 [20] | NSAIDs | LL/? | 0.67 (0.44, 0.90) | 65.8% | <0.01 | 9 | |
| Szekely 2004 [25] | NSAIDs-lifetime exposure | ML/LL/? | 0.74 (0.62, 0.89) | – | absent∧ | 4 | |
| Wang 2015 [78] | All NSAIDS | LL/? | 0.69 (0.56, 0.86) | 79.7% | 0.10 | 12 | |
| Etminan 2003 [79] | All NSAIDs | ML/LL | 0.84 (0.54, 1.05) | 62.3% | 0.95 | 6 | |
| HRT | LeBlanc 2001 [80] | Any use versus never use | LL | 0.50 (0.30, 0.80) | 0.0% | ∼ | 2 |
| Xu 2015 [20] | Any use versus never use | LL/? | 0.61 (0.46, 0.76) | 38.1 | <0.01 | 4 | |
| O’Brien 2014 [81] | Any use versus never use | ? | 0.69 (0.48, 1.00) | 31.4% | 0.78 | 8 | |
| Insulin sensitizers | Ye 2016 [82] | Insulin-sensitizers versus non-insulin sensitizers | ? | 0.90 (0.55, 1.45) | – | unobvious∧ | 2 |
| Statins | Zhou 2007 [83] | Any use versus non-user | ? | 0.90 (0.65, 1.25) | 0.0% | ∼ | 3 |
| Xu 2015 [20] | Current use versus never use | LL/? | 0.59 (0.45, 0.73) | 26.4% | 0.29 | 5 | |
| Xu 2015 [20] | Former versus never use | ? | 1.28 (0.69, 3.24) | 74.6% | ∼ | 2 | |
| Xu 2015 [20] | Longer use versus never use | ? | 0.24 (0.07, 0.70) | 0.0% | ∼ | 2 | |
| Wong 2013 [84] | Users versus non-users | ? | 0.70 (0.60, 0.80) | 18.2% | minimal∧ | ||
| Richardson 2013 [35] | Users versus non-users | ML/LL/? | 0.79 (0.63, 0.99) | 91.6% | 0.38 | 10 | |
| Environmental | |||||||
| Pesticides | Yan 2016 [85] | Pesticide exposure | LL/? | 1.37 (1.08, 1.75) | 0.0% | 0.66 | 3 |
| Xu 2015 [20] | Occupational exposure to pesticides | LL/? | 1.26 (0.93, 1.59) | 5.4% | 0.78 | 3 |
Note.*the primary age represented per pooled effect (RR) is denoted by bold text. ‘adj’ denotes age-adjusted (baseline age is not relevant to measures of self-reported educational attainment), ‘ML’ denotes midlife (baseline age < 65), ‘LL late-life (baseline age 65+) and ‘?’ unknown. ‘RR’ denotes risk ratio, which is the pooled effect size. ‘–’ denotes not reported. ‘∼’ indicates there were too few primary studies to calculate Egger’s p. ∧bias as indicated by visual inspection of funnel plot. Egger’s values are as reported in primary reviews, but not a recommended measure of bias when for n < 10. ‘n’ is the number of primary studies included in the meta-analysis for each RR.